The in the study area. And nearly half

The
main objective of the study is to assess the impact of women’s socio-economic status on fertility
in Bangladesh. In addition, the role of various socio-economic variables are
also explored in order to have a comprehensive understanding of the dynamics of
fertility. Selected socio-economic variables include age,
education, property and wealth, occupation, income, religion, residence, media
coverage. The data was collected through face to face interview using
structured questionnaire from the selected field in Raipura Pawrasava and
Srinagar union under Narsingdi district. In this study, Univariate, Bivariate
and multivariate analyses are done to identify, categorize and analyze the
socio-economic variables and to determine the main driver of fertility. The
findings from the analysis indicate that age, education, property and wealth,
occupation, income, religion, residence, media coverage of the respondents are
the most important variables in explaining differential of fertility in
Bangladesh.

 

 

In
univariate analysis, expresses the social, economic, demographic
characteristics and fertility level of the respondents. From the total
respondents, percentage of married women is high in 25-29 and 30-34 age group
(25.6% and 24.0% respectively). According to age at first marriage, less than
18 years age group (72.9%) is higher than 18 and above age group (27.1%). From
the total married women, 93% of married women gave birth. According to age at
first birth, less than 20 years age group (49.6) is nearly half.  And the rest percentage is the 20 and above
years age group (50.4). So, in the study, it is found that child marriage rate
is so high (72.9%) in the study area. And nearly half of the married women give
birth before 20 years age (Table 1 and 14).

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

In
the study, 22.5 percent married women had no education and nearly half
respondents did not cross primary school. Only 22.5 percent married women had
higher education (Table 1).

The
situation is more vulnerable in the husband’s education. Around 30% respondent
husbands had no education and more than half of respondent husband did not
cross primary level. Only 21.7 percent respondent husband had higher education
(Table 1).

 

In
the study, 82.2 percent respondents are Muslim and the rest 17.8 percent are
Hindu. And the rural-urban proportion is nearly same. More than half percent of
the respondents (55%) have media access and nearly half of them (45%) have no
access to media (Table 1).

According
to economic characteristics of the respondents, most of the married women had
no income (83.7%) as they had no paid job (84.5% housewife). Only 16.3 percent
married women had income in the study. Married women mostly depended on their
husband but most of them was day labor (47.3%) (Table 2).

 

Desired
and observed fertility level of the respondents coded into two variable: high
(more than 2 child) and low (2 and less than 2 child) fertility. From the total
married women 50.4 percent had low fertility and the rest 49.6 percent had the
high fertility. And it is very interesting that desired high fertility (55%) is
higher than observed high fertility (49.6) (Table 4). The reasons behind this
may be the religious views “who gives mouth, gives food also” is strongly
present in the rural study area. And the other reason would be newly married
women and one child mother want to more child.

 

 

In
bivariate analysis, assess the relationship between independent variables and
dependent variable. The relationship between respondent education and fertility
is negatively correlated. Fertility rate is high among the respondents those
who had no education. The rate is decreasing gradually by increasing of
education level among respondents. Fertility rate is very low among the
respondents those who had higher education (Figure 3).

 

There
is a relationship between respondent’s occupation and fertility. . In these case, observed and desired fertility is low among
service holder and other group and observed and desired fertility is high among
housewife group (Figure 5).

 

There
is a strong relationship between respondent’s income and fertility. In the
case, observed and desired fertility is low
among has income group and high among no income group (Figure 6). On the other hand,
there is a relationship between respondent husband income and fertility. In
these case, observed and desired fertility is low among high income group
comparatively low income group (Table 9). But the magnitude of respondent’s
income is higher than respondent husband’s income on fertility.

 

There
is also relationship between respondent’s residence and fertility. In these case, observed and desired fertility is low among urban
group comparatively rural group, but magnitude of this variable is not so high
(see Table 10). As the residence of the respondents, there is also relationship
between religion and fertility. In the case, observed and desired fertility is
low among Hindu group comparatively Muslim group, but magnitude of this
variable is not so high (Table 11).

 

There
is a strong relationship between media access and fertility. In the case, observed and desired fertility is high among the
respondents those who have no media access comparatively the respondents those
who have media access (Figure 10). There is a positive relationship between
respondent husband’s property & wealth and fertility. In the case, observed
and desired fertility is high among respondents whose husband’s property &
wealth is high comparatively the respondents whose husband’s property &
wealth is low (Table 13).

 

In OLS regression model, it is found that age, occupation and media access have
positive effect on number of children in rural area and age at first marriage,
education and husband income have negative effect on number of children. On the
other hand, age, education, occupation and religion have positive relationship
on number of children in terms of reference variables in urban area and age at
marriage, husband income and media access have negative effect on fertility (Table
19).

In
this study, it is found that 85.9 percent rural respondents are married within
less than 18 years of age. On the other hand, 40.0 percent urban respondents
are married less than 18 years of age. In this case, most of the child marriage
happened in rural area and it is higher (more than double) than urban area. But
first age at birth of the respondents who were less than 20 years, was nearly
same between rural (53.1%) and urban (46.2%) areas (Table 15).

 

In
this study, respondent’s education coded into four categories. Most of the
respondents who live in rural area had no education (40.6), but who are in
urban area had only 4.6 percent no education. On the other hand, secondary and
higher education participation rate also very low in rural area (25% and 6.3%
respectively) than urban area (36.9% and 38.5% respectively). The same
condition in the respondent husband’s education like the respondent’s
education. In this study, media access rate was very low (10.9%) in rural area.
Nearly 90% respondents have no media access. On the other hand, media access
rate was so high (98.5%) among the urban respondents. In every social
characteristics, urban area was better than rural area (see Table 16).