Along with the developing world, Bangladesh has made a tremendous improvement in declining total fertility rate, however, this declining trend is not uniform to all the socio-demographic stratum. Incongruities exist between the numbers of children that women bearing and what they actually desired which refers to unmet fertility desire. This study aims to elicit women’s perception of ideal number of children and predictors of unmet fertility desire in Bangladesh.
This study analyzed nationally representative cross-sectional Bangladesh Demographic and Health Survey 2014 data. A two-stage stratified random sampling technique was used while a total of 17,863 ever-married women were interviewed between June and November 2014. A total of 10,912 eligible women were included in the analysis. Poisson regression analysis and logistic regression models were used to measure women’s perception of the ideal number of children and to determine the influencing factors of unmet fertility desires.
The mean value of the perceived ideal number of children was 2.22 (SD ± 0.73) and the majority of women (71.2%) expect to have two children in their lifetime. Approximately 46% of mothers reported bearing more children than they desired. The perceived ideal number of children was significantly higher among women who were living in rural areas, from Sylhet division, Muslim, unemployed, and experienced child death and those who justified beating. Findings revealed that several factors such as place of residence, geographic location, religion, wealth index, maternal age and education, partners’ education, experiencing child death, and other empowerment-related indicators were significantly associated with unmet fertility desires.
Perceived ideal number of children differs among women’s socioeconomic and demographic strata. Unmet fertility desire was also found which indicates that reproductive knowledge and health care services are still necessary for some socio-demographically disadvantaged/vulnerable people and this group should be regularly monitored to control population growth.
The data underlying the results presented in the study are available from
There is ample evidence of declining total fertility rate (TFR) worldwide including developing countries. For instance, TFR had declined globally from 4.98 to 2.4 from 1960 to 2017, almost half by 60 years [
Many studies have identified the association of women’s socio-economic and decision-making autonomy with the use of contraceptives, intimate partner violence, and health-care services on fertility decline. However, none of them focused on fertility desires and determinants of unmet fertility needs in the Bangladeshi context. For instance, Duvendack and colleagues (2016) showed the association of women empowerment and the trend of fertility among women by years’ span [
Based on the available literature, it is evident that assessing the determinants of fertility intension and exploring the extent to which they are associated with having more than desired children are crucial for the performance of family planning programs initiatives and for the population policy of a country [
Despite the growing importance of fertility desires and unmet need, little is known about their actual desires, unmet fertility need with associated predictors including women's autonomy related indicators are rarely found in the context of Bangladesh using nationwide survey data. To mitigate these gaps, this study intended to get further insights into the current fertility desires with unmet fertility needs in terms of women’s perception of the ideal number of children by disaggregating socio-demographic, and women's autonomy related indicators. Analysis from the latest nationwide demographic and health survey data would thus allow us for generating evidence on how the fertility preference is distributed across socio-demographic groups and by women's autonomy related factors. It is expected that the evidence generated from the findings would be useful for family planning policy implication targeting the most appropriate population of Bangladesh.
Bangladesh Demographic and Health Survey (BDHS) is a part of the long-standing worldwide Demographic and Health Survey (DHS) program which is conducted every 3 years and captures information covering individual and household-level health and demographic data nationwide. This study used the latest (seventh) round of nationally representative BDHS 2014 dataset for analysis. A wide range of information was collected through questionnaire-based, face-to-face interviews, where reproductive-age women (15–49 years) were interviewed based on the MEASURE DHS program model [
Explanatory variables were selected based on a literature review, prior knowledge, and the availability of variables in the Bangladesh Health and Demography Survey (BDHS) 2014 dataset. These variables included the area of residence, administrative divisions, wealth quintile, religious view, respondent age, respondent education, partner education, currently living children, experience with child death, types of contraception use, involvement in household decision making, beating justification (the attitude of women toward being beaten by their husband), NGO membership, control over earnings, and access to mass media. In this analysis, categorization was performed for several continuous variables. Since it is well established that maternal age is an important factor in reproduction, and as of many other earlier studies maternal age was categorized by five years of interval. This will allow getting insights into the change of fertility behavior of women of different age groups. Therefore, respondent’s age was categorized into five groups (15–25 years, 26–30 years, 31–35 years, 36–40 years, and over 40 years). The education level of the women and their partners were categorized into four groups: “no education,” “primary”, “secondary”, and “higher”. No education refers to not attaining any formal education, while primary education is defined as completing grade 5, secondary as completing grade 10, and higher as attaining more than a grade 10 education. We utilized the predetermined wealth index category provided with the dataset, which was generated from selected household assets using principal component analysis (PCA) and classified into five groups: “poorest”, “poorer”, “middle”, “rich”, and “richest”. The types of contraception use were categorized into three groups: “no method”, “traditional method” and “modern method”. No method is defined as neither the respondent nor her husband using any contraception during intercourse, while the traditional method comprises of periodic abstinence, and the modern method includes the birth control pill, injectable birth control, condoms, male or female sterilization, intrauterine contraceptive device (IUD), and implants.
The women’s empowerment-related variables used in this analysis include the number of household decisions in which women participated, attitude toward being beaten by their husband, membership in any NGOs, and control over income. To access women’s decision-making ability, they were asked five questions: participation in decision making regarding i) their own health care, ii) major household purchases, iii) child’s health care, iv) visits to their family and relatives, and v) using contraception. Attitude toward family violence (e.g., being beaten by their husband), which was presented as beating justification and categorized as either “yes” or “no”. Beating justification was categorized as yes if the woman justifies beating by her husband for any of five reasons (i.e., if goes outside without telling her husband, neglects her children, argues with her husband, refuses to have sex, or burning food). If the woman justifies beating for none of the following reasons, then it was categorized as no. Moreover, membership in any NGOs and involvement in any income generation activities with control over her earnings were also categorized and included in the analysis.
The outcome variables of the study include “perceived ideal number of children” and “unmet fertility desire”. For this study, we analyzed data of those women who wanted no more children, were sterilized or declared infecund, and provided numerical answers for fertility desire (ideal number of children). These criteria were used to restrict the sample to women who have theoretically completed their reproductive age meaning that according to their statement they are unable or strongly unwilling to conceive as of their current status. These restrictions yielded a sample size of 10,912 eligible participants. The DHS dataset gathered information on the women’s perceptions of the ideal number of children by asking different questions to the respondent(s). Women with living children were asked, “If you could go back to the time when you did not have any children and could choose exactly the number of children to have in life, how many would that be?” Women with no children at the time of the survey were asked, “If you could choose exactly the number of children to have in life, how many would that be?” Another outcome variable, “unmet fertility desire”, that means having more children than they desired was defined as the difference between the desired and actual number of children a respondent had during the time of the survey [
Datasets were checked for missing values and outliers prior to analysis. A proper sampling weight provided with the dataset by MEASURE DHS was applied in this analysis to make the sample more representative of the population across different areas of the country. Descriptive statistics, such as frequency distribution in terms of percentages and 95% confidence interval (CI) were estimated to outline the background characteristics of study participants. The association between women’s perception of the ideal number of children and selected demographic and empowerment-related variables was investigated using the Poisson regression model. Two logistic regression models; Model I and Model II were constructed to predict the association of socio-demographic variables with women’s unmet fertility desires. Model I stands for the bivariate logistic regression model representing the crude association of dependent variable with each of the independent variables (unmet fertility desire) where the Model II constructed the multivariate logistic regression model while all of the explanatory variables were adjusted simultaneously with the dependent variable. Variables that showed a significant association in the bivariate logistic regression analysis were added into the multivariate logistic regression model. Diagnostic tests were employed in the analysis. Variance Inflation Factor (VIF) was calculated to detect multicollinearity in the model. The low value of average VIF for both the Poisson regression (3.09) and logistic regression (2.98) confirms no notable multicollinearity among variables. In the Poisson regression analysis, the goodness-of-fit chi-squared test is not statistically significant that indicated that the model fitted reasonability well. For the logistic regression model, linear predicted value (_hat) and linear predicted value squared (_hatsq), determined using
BDHS 2014 is a publicly available dataset and can be downloaded from the DHS Program website (
The description and distribution of study participant characteristics for this analysis are presented in
Characteristics of Sample | Percentage (%) (n = 10,912) | 95% CI | |
---|---|---|---|
27.04 | 26.22 | 27.89 | |
72.96 | 72.11 | 73.78 | |
12.63 | 12.02 | 13.27 | |
6.26 | 5.82 | 6.73 | |
17.36 | 16.66 | 18.08 | |
33.75 | 32.87 | 34.64 | |
11.13 | 10.56 | 11.74 | |
12.41 | 11.80 | 13.04 | |
6.46 | 6.01 | 6.94 | |
19.95 | 19.21 | 20.71 | |
20.13 | 19.38 | 20.89 | |
19.91 | 19.17 | 20.67 | |
20.09 | 19.35 | 20.85 | |
19.93 | 19.19 | 20.69 | |
89.06 | 88.46 | 89.63 | |
9.09 | 8.56 | 9.64 | |
1.86 | 1.62 | 2.13 | |
1.87 | 1.63 | 2.14 | |
9.90 | 9.36 | 10.48 | |
18.61 | 17.89 | 19.35 | |
22.41 | 21.64 | 23.21 | |
18.06 | 17.34 | 18.79 | |
16.10 | 15.42 | 16.80 | |
13.05 | 12.44 | 13.70 | |
30.98 | 30.12 | 31.86 | |
31.60 | 30.73 | 32.48 | |
31.62 | 30.75 | 32.5 | |
5.80 | 5.38 | 6.25 | |
33.99 | 33.11 | 34.89 | |
27.31 | 26.49 | 28.16 | |
26.20 | 25.38 | 27.03 | |
12.49 | 11.89 | 13.13 | |
45.36 | 44.43 | 46.3 | |
43.35 | 42.43 | 44.29 | |
11.28 | 10.7 | 11.89 | |
79.1 | 78.32 | 79.85 | |
20.9 | 20.15 | 21.68 | |
31.66 | 30.79 | 32.54 | |
10.13 | 9.58 | 10.71 | |
58.21 | 57.28 | 59.13 | |
40.87 | 39.95 | 41.79 | |
59.13 | 58.21 | 60.05 | |
6.18 | 5.75 | 6.65 | |
21.38 | 20.62 | 22.16 | |
40.49 | 39.58 | 41.42 | |
31.94 | 31.08 | 32.83 | |
71.35 | 70.49 | 72.19 | |
28.65 | 27.81 | 29.51 | |
61.69 | 60.77 | 62.59 | |
38.31 | 37.41 | 39.23 | |
65.68 | 64.78 | 66.56 | |
4.66 | 4.28 | 5.07 | |
29.66 | 28.81 | 30.53 | |
46.21 | 45.27 | 47.14 | |
53.79 | 52.86 | 54.73 | |
7.27 | 6.8 | 7.78 | |
71.21 | 70.36 | 72.05 | |
21.51 | 20.75 | 22.29 | |
CI: confidence interval; NGO: non-government organization; SD: standard deviation; No formal education, primary, secondary, and higher education refers to not attaining any formal education, completing grade 5, grade 10, and completing higher than grade 10, respectively.
Results from the linear regression model describe the association of women’s perceptions of the ideal number of children with selected demographic and empowerment-related variables (
Characteristics of Sample | Coefficient (95% CI) | Robust SE | |
---|---|---|---|
- | |||
0.04 (0.02, 0.06) | 0.01 | ||
- | |||
0.08 (0.05, 0.10) | 0.01 | ||
0.15 (0.13, 0.18) | 0.01 | ||
0.05 (0.03, 0.07) | 0.01 | ||
-0.02 (-0.04, 0.01) | 0.01 | 0.141 | |
0.02 (0.01, 0.05) | 0.01 | ||
0.18 (0.15, 0.21) | 0.02 | ||
0.05 (0.02, 0.08) | 0.02 | ||
0.03 (0.01, 0.06) | 0.01 | ||
0.02 (0.00, 0.05) | 0.01 | 0.077 | |
0.04 (0.01, 0.06) | 0.01 | ||
- | |||
- | |||
-0.08 (-0.1, -0.06) | 0.01 | ||
-0.1 (-0.17, -0.02) | 0.04 | ||
0.21 (0.13, 0.30) | 0.40 | ||
0.28 (0.20, 0.37) | 0.40 | ||
0.35 (0.27, 0.43) | 0.40 | ||
0.39 (0.30,0.47) | 0.40 | ||
0.40 (0.31, 0.49) | 0.40 | ||
0.45 (0.36, 0.53) | 0.40 | ||
0.10 (0.06, 0.14) | 0.02 | ||
0.07 (0.03, 0.10) | 0.02 | ||
0.04 (0.01, 0.08) | 0.02 | ||
- | |||
0.01 (-0.04, 0.03) | 0.02 | 0.825 | |
0.01 (-0.03, 0.03) | 0.02 | 0.902 | |
-0.02 (-0.04, 0.01) | 0.01 | 0.273 | |
- | |||
- | |||
0.04 (0.02, 0.06) | 0.01 | ||
0.01 (-0.01, 0.03) | 0.01 | 0.255 | |
0.01 (-0.02, 0.04) | 0.01 | 0.397 | |
- | |||
0.03 (0.01, 0.05) | 0.01 | ||
- | |||
0.04 (-0.01, 0.08) | 0.02 | 0.110 | |
0.01 (-0.02, 0.03) | 0.01 | 0.644 | |
0.01 (-0.02, 0.02) | 0.01 | 0.733 | |
- | |||
- | |||
0.02 (0.01, 0.04) | 0.01 | ||
0.01 (0.00, 0.03) | 0.01 | 0.104 | |
- | |||
0.03 (0.01, 0.05) | 0.01 | ||
0.02 (-0.01, 0.06) | 0.02 | 0.196 | |
- |
Regarding the empowerment indicators, a desire for more children was observed among mothers who justified beating for any reason (coefficient: 0.02, CI: 0.01, 0.04;
Findings from the logistic regression analysis presented in
Characteristics of Sample | Unadjusted model (Model I) | Adjusted model (Model II) | ||
---|---|---|---|---|
OR (95% CI) | OR (95% CI) | |||
| 1.00 | 1.00 | ||
| 1.39 (1.27, 1.51) | 1.11 (0.99, 1.23) | 0.067 | |
| 1.00 | 1.00 | ||
| 1.51 (1.26, 1.82) | 1.59 (1.30, 1.95) | ||
| 1.69 (1.47, 1.94) | 2.32 (1.98, 2.73) | ||
| 1.29 (1.14, 1.46) | 1.47 (1.28, 1.69) | ||
| 1.07 (0.91, 1.25) | 0.420 | 1.12 (0.95, 1.33) | 0.181 |
| 1.13 (0.97, 1.31) | 0.119 | 1.13 (0.96, 1.34) | 0.140 |
| 2.02 (1.68, 2.43) | 2.20 (1.80, 2.70) | ||
| 1.95 (1.73, 2.21) | 1.60 (1.32, 1.92) | ||
| 1.64 (1.46, 1.86) | 1.19 (1.00, 1.41) | 0.056 | |
| 1.45 (1.28, 1.63) | 1.09 (0.93, 1.28) | 0.274 | |
| 1.26 (1.12, 1.42) | 1.01 (0.87, 1.16) | 0.936 | |
| 1.00 | 1.00 | ||
| 1.00 | 1.00 | ||
| 0.6 (0.53, 0.69) | 0.58 (0.5, 0.67) | ||
| 0.5 (0.37, 0.68) | 0.31 (0.22, 0.43) | ||
0.02 (0.01, 0.05) | 0.02 (0.01, 0.04) | |||
0.12 (0.10, 0.15) | 0.09 (0.08, 0.12) | |||
0.31 (0.27, 0.36) | 0.27 (0.23, 0.32) | |||
0.61 (0.53, 0.69) | 0.54 (0.47, 0.63) | |||
0.70 (0.61, 0.80) | 0.64 (0.55, 0.74) | |||
0.87 (0.75, 1.00) | 0.79 (0.68, 0.92) | |||
1.00 | ||||
| 5.92 (4.80, 7.31) | 2.65 (2.03, 3.45) | ||
| 4.28 (3.47, 5.28) | 2.56 (1.98, 3.31) | ||
| 2.36 (1.91, 2.91) | 2.09 (1.64, 2.65) | ||
| 1.00 | 1.00 | ||
| 3.07 (2.69, 3.51) | 1.65 (1.36, 2.00) | ||
| 2.45 (2.13, 2.81) | 1.69 (1.41, 2.02) | ||
| 1.67 (1.45, 1.92) | 1.34 (1.14, 1.58) | ||
| 1.00 | 1.00 | ||
| 1.00 | 1.00 | ||
| 1.48 (1.35, 1.63) | 0.89 (0.80, 0.99) | ||
| 1.14 (1.05, 1.24) | 0.93 (0.83, 1.03) | 0.171 | |
| 1.17 (1.03, 1.33) | 0.92 (0.80, 1.06) | 0.239 | |
| 1.00 | 1.00 | ||
| 1.63 (1.51, 1.76) | 1.10 (0.99, 1.22) | 0.072 | |
| 1.00 | 1.00 | ||
| 0.87 (0.73, 1.02) | 0.091 | 0.82 (0.67, 1.01) | 0.058 |
| 1.08 (0.97, 1.20) | 0.170 | 1.07 (0.94, 1.21) | 0.289 |
| 1.14 (1.04, 1.25) | 1.02 (0.91, 1.14) | 0.752 | |
| 1.00 | 1.00 | ||
| 1.00 | 1.00 | ||
| 1.07 (0.98, 1.16) | 0.129 | 0.92 (0.84, 1.01) | 0.087 |
| 0.86 (0.80, 0.93) | 0.90 (0.83, 0.99) | ||
| 1.00 | 1.00 | ||
| 1.02 (0.94, 1.11) | 0.652 | 1.17 (1.07, 1.28) | |
| 0.91 (0.75, 1.10) | 0.319 | 0.91 (0.74, 1.11) | 0.345 |
| 1.00 | 1.00 |
®: Reference category; OR: odds ratio; CI: confidence interval
1 Model I represents findings from bivariate logistic regression analysis; and
2 Model II shows the findings from the multivariate logistic regression analysis.
In the adjusted model (Model II), it was observed that the highest odds of having more children than desired were in Chittagong division (OR = 2.32; CI: 1.98, 2.73) followed by Sylhet (OR = 2.20; CI: 1.8, 2.7) and Barisal (OR = 1.59; CI: 1.30, 1.95), when compared to Rajshahi division. Significantly, mothers from the poorest quintile were 1.60 times more likely to have more children than they desired (CI: 1.32, 1.92;
Unadjusted model (Model I), based on empowerment-related indicators, our study determined that respondents who participated in three to four household decisions were more likely (OR = 1.14; CI: 1.04, 1.25;
Bangladesh has made tremendous improvements in reducing TFR substantially to restrict excessive population growth. Despite this, our findings revealed differences between perceived ideal number of the family size and the actual number of children for households. For instance, although the majority of the mothers (71%) perceived two children as an ideal number, only 45% reported that they limit their number of children up to two. About half of the respondents experienced unmet fertility desires having more children than they expected to have initially. Several factors such as geographic location, religion, wealth index, age, education, experiencing child death, and women empowerment indicators were significantly associated with mothers’ desired number of children and unmet fertility desires.
The findings of our study revealed that most mothers (71.2%) expected to have two children for their family, which is in line with the population policy of Bangladesh [
We observed a significant relationship between religion and fertility desire, where women with Muslim religious views have greater unmet fertility needs. This finding is confirmed by previous study findings, where similar associations were also reported. For instance, a study conducted on Saudi women observed that religious prohibition is one of the most significant barriers to using any type of contraceptives [
The present study also reveals that there is a significant relationship between the unmet need for fertility and the age and educational status of mothers. The positive relationship between the level of education and public health awareness is well established [
While providing many useful results, the present study had several limitations. First, this study was based on cross-sectional data, which limits the establishment of a causal relationship. Second, this study included only five aspects of women’s participation in decision-making to explore perception, and respondents were only women. As a result, there may be potential discordance regarding the level of autonomy and women’s empowerment status, which may remain underestimated. Therefore, it may be prudent to collect information from both women and men in future studies to generate a more reliable picture of women’s empowerment. However, we were unable to explore men’s perception due to data unavailability in the DHS survey. Despite these limitations, the primary strength of this study includes the national representation of findings, as the latest national representative DHS dataset was used for this analysis. Therefore, the findings remain noteworthy and relevant in drawing attention to policymakers for the betterment of family planning and maternal health.
Tracking the unmet need for fertility desire is useful for assessing progress towards the target of achieving universal access to reproductive health in Bangladesh. Findings revealed that the perceived ideal number of children differs among women’s socioeconomic and demographic strata. Unmet fertility desire was also found which indicates that reproductive knowledge and health care services are still necessary. Several factors including age, religion, maternal and paternal education, socioeconomic strata, administrative regions and some empowerment-related indicators like participation of household decision making, membership of any NGOs and involvement of any income generation activities were significant influencing factors of unmet fertility desire among Bangladeshi women. Periodical assessment and monitoring the level of unmet fertility desire is recommended particularly in low-performing regions so that all strata of the society can benefit.
icddr,b is thankful to the Governments of Australia, Bangladesh, Canada, Sweden, and the UK for providing core/unrestricted support. The authors also acknowledge Bangladesh Institute of Development Studies (BIDS) for the staff time involvement to develop the manuscript.