Human Fertility Behaviors in Bangladesh: A Multivariate Statistical Analysis

Human Fertility Behaviors in Bangladesh: A Multivariate Statistical Analysis MD. MOSHARAF HOSSAIN & J. A. M. SHOQUILUER RAHMAN Departments of Population Science & HRD, University of Rajshahi Rajshahi 605, Bangladesh E-mail: m_population@yahoo.com E-mail: shahin67_ru@yahoo.com KEY WORDS: Human fertility behavior. Bangladesh. Socio-economic and demographic variables. Logistic regression analysis. ABSTRACT: The impacts of different socio-economic and demographic variables on fertility bahaviours among Bangladeshi woman have been studied. The age range of the subjects was between0-49 years. Bangladesh Demographic and Health Survey (BDHS) collected data in 007 from various districts of Bangladesh. Chi-square test and logistic regression analysis have been used to the data. The analysis demonstrates that seven variable: marital status, respondent s education, partner education, region, residence, religion, index of health, and a few other variables are statistically significant on fertility. It may concluded from the study that fertility control measures need urgent attention in Bangladesh. INTRODUCTION term reduction in fertility of a country Bangladesh is a small country of 47570 square (Mukherjee, 75). kilometers areas with a population of around 56 The relationship between age at marriage and millions people in 00 (CIA, 0). Human fertility fertility is well known (Maudlin and Berelson, 78; is responsible for the biological replacement and Pandey and Talwar, 87). As age at cohabitation (i.e. maintenance of the human species. In fact, the fertility age at consummation of marriage) determines the is a major counteracting force to population attrition reproductive life span of a woman which has a direct from mortality and therefore, has a significant impact bearing on fertility, and it is one of the important as an expansionary force in population dynamics. aspects with regards to fertility (Maudlin and Fertility may be defined as the actual reproductive Berelson, 78; Nag, 8; Chaudhury, 84). It is found performance of a woman or group of women that at a later age marriage reduces fertility (Thompson and Lewis, 65). Woman s education in (Agarwala, 67; Durch, 80; Yadav and Badari, 97). Bangladesh plays a very important role in the overall Educational level, economic status, religious attitudes, development of the country. Educated woman women s work participation etc. are other factors can help in reduction of fertility and growth of affecting fertility (Basu et al., 88; Bhasin, 90; Elamin population. The status of woman and their role in and Bhuyan, 99; Pandey et al., 000; Bhasin and Nag, decision making in reproduction have an important 00), in addition to, conception control practice and bearing on the success of family planning and the long- attitudes (Bhuyan and Ahmad, 84). The purpose of the present study is to assess the Research Fellow Professor impact of different socio-economic and demographic factors on human fertility behaviors in Bangladesh. South Asian Anthropologist, 0, (): -6 New Series SERIALS

Md. Mosharaf Hossain & J. A. M. Shoquiluer Rahman MATERIALS AND METHODS Materials: The total sample used in the current study consisted of 0996 Bangladeshi married women. Age of the subjects at the time of collecting the data ranged from 0-49 years. Bangladeshi Demographic and Health Survey (BDHS) collected the data in 007 from the various districts of Bangladesh using multistage cluster sampling technique. Seven socio-economic and demographic variables were collected from the subjects as shown in Table. Model Building (Methods): Cox ( 70) discovered the logistic regression model that can be used not only to identify risk factors but also to predict the probability of success. Furthermore, Lee ( 80) and Fox ( 84) developed this model. This model expresses a qualitative dependent variable as a function of several explanatory variables, both qualitative and quantitative. Logistic regression is a form of regression, which is used when the dependent is a dichotomy and the independents are of any type. In logistic analysis index of fertility is treated as dependent variable. In this analysis to create a dependent variable as well as fertility measurement variable have calculated median of the variable name total number of children ever born per woman aged 0-49 years. Then this variable has been coded as 0 for above median and for less or equal to median. To make literally meaningful the new variable is treated as index of fertility and its two levels are accordingly disfavorable fertility behavior instead of above median, and favorable fertility behavior instead of less or equal to median. Then the dependent variable index of fertility is shown to be binary or dichotomous one. The fertility behavior or fertility rate of Bangladeshi woman is indicated by the index in this analysis. When it takes the value the probability will be P (say) if the respondent exits favorable fertility behavior, and 0 with probability (- P) if he/she exits disfavorable fertility behaviors. The considering models and the dependent variables chosen in the model are given in the following: 0, disfavorable fertility Yi ; i,,3,..., favorable fertility Division, religion, respondent s education, partner education, mass media, index of wealth, contraceptive methods, currently breastfeeding martial status and index of fertility are considered as explanatory variables in this model. Dependent Variable Index of Fertility TABLE Description of variables in this study Variables Y=0, Disfavourable fertility behaviour Y=, favourable fertility behaviour Independent Variables Division Dhaka=X, Chittagong=X, Rajshahi=X 3, Barisal=X 4, Sylhet=X 5 & Khulna=X 6 Places of Residence Urban=X & Rural=X Respondents Education No-education=X, Primary education=x & Secondary and higher=x 3 Partner education No-education=X, Primary education=x, Secondary and higher=x 3 Religion Muslim=X & Non-muslim=X Marital Status Currently Married=X & Others=X Ever Used any Methods Never Used=X & Others=X Wealth Index Poor=X, Middle=X & Rich=X 3 Currently Breastfeeding No=X 0 & Yes=X Reading Newspaper Not at all=x & At all=x Listening Radio Not at all=x & At all=x Watching TV Not at all=x & At all=x RESULTS AND DISCUSSION Fertility is a most important factor in population dynamics as it affects age at marriage tremendously and mortality and migration to a lesser extent. In Bangladesh, where marriage is nearly universal, age at marriage has a strong influence on a variety of demographic, social and economic factors. This study will be able to pay a greater attention to find out those factors that are influencing the age at marriage in Bangladesh. To see the association between fertility and various selected background characteristics in Bangladesh, a well-known statistical tool namely- Pearson Chi-square test procedure was used and the results are presented in Table. The results revealed that there were significant variations in legal fertility among the socioeconomic and demographic characteristics. Among the selected background characteristics-respondent s education partner, martial status, every used any method, wealth index, currently breastfeeding, listening

Human Fertility Behaviors in Bangladesh: A Multivariate Statistical Analysis 3 TABLE Chi-square ( ) test of index of fertility among various socio-economic and demographic variables Variables Index of Fertility values Favorable Disfavorable Division Dhaka 340(.3%) 50(.8%).493 Chittagong 943(7.7%) 98(7.3%) Rajshahi 080(8.9%) 08(8.%) Barisal 438(3.%) 45(.6%) Sylhet 3(3.4%) 63(4.%) Khulna 7(5.6%) 83(6.0%) Places of Residence Urban 3695(37.5%) 456(39.8%).9 Rural 654(6.5%) 69(60.%) Respondents Education No-education 3346(34.0%) 79(5.6%) 95.048 * Primary 3000(30.5%) 7(3.6%) Secondary and higher 3503(35.6%) 697(60.8%) Partner education No-education 3356(34.%) 53(.%) 77.834 * Primary 58(6.%) 308(6.9%) Secondary and higher 39(39.7%) 586(5.%) Religion Muslim 09(89.7%) 8(0.3%) 0.4 Non-muslim 8895(90.5%) 954(9.7%) Marital Status Currently Married 07(89.3%) 0(0.5%) 3.4 * Others 99(9.6%) 730(7.4%) Ever Used any Methods Never Used 645(6.7%) 63(53.4%) 849.903 * Others 804(83.3%) 534(46.6%) Wealth Index Poor 3458(35.%) 3(7.%) 30.6 * Middle 870(9.0%) 5(9.6%) Rich 45(45.9%) 60(53.%) Currently Breastfeeding No 6645(67.5%) 47(00.0%) 56.56 * Yes 304(3.4%) 0(0.0%) Reading Newspaper Not at all 8343(84.7%) 836(7.9%) 04. * At all 506(5.3%) 3(7.%) Listening Radio Not at all 767(77.9%) 773(67.4%) 63.608 * At all 77(.%) 374(3.6%) Watching TV Not at all 446(45.3%) 4(36.8%) 30.083 * At all 5388(54.7%) 75(63.%) Notes: * p<0.00 radio, reading newspaper and watching TV were significantly associated with the fertility in Bangladesh. The results of logistic regression analysis are presented in Table 3 in the form of logistic regression coefficients, p-value, and relative odds ratio corresponding to the selected explanatory variables. Dhaka division was considered as the combination and interactions of all other divisions, therefore, considering Dhaka as reference category. The odds ratio corresponding to Chittagong, Rajshahi, Barisal, Sylhet, Khulna divisions were found.30, 0.840, 0.84, 0.855 and.68 respectively. This clearly indicated that the Dhaka divisions had.00 times more probability in getting fertility than the others division only Chittagong division it was.30 time more fertility in Dhaka division. It is apparent from the results that there are significant regional variations in fertility among the population of Bangladesh. Therefore, to order to achieve the replacement level fertility within 05, various programs should be taken to remove the regional variations in fertility among the women of Bangladesh. Education is the key determinant of the life style and status enjoys in a society. For this reason, the regression coefficients corresponding to different levels of education both for respondents and their husbands were calculated. It was seen that the results had statistically significant effect on fertility. From the results, it is also evident that the respondents with primary level education, and secondary and higher education levels have less fertility, than their counterparts with no education.

4 Md. Mosharaf Hossain & J. A. M. Shoquiluer Rahman TABLE 3 Results of logistics analysis of index of fertility among various socio-economic and demographic variables Variables Exp() Division Dhaka(RC).000 Chittagong.30 ** Rajshahi 0.840 Barisal 0.84 Sylhet 0.855 Khulna.68 Places of Residence Urban(RC).000 Rural.096 Respondents Education No-education(RC).000 Primary education 0.40 * Secondary and higher 0.3 * Partner education No-education(RC).000 Primary education 0.78 ** Secondary and higher.00 Religion Muslim(RC).000 Non-muslim 0.83 Marital Status Currently Married(RC).000 Others.498 * Ever Used any Methods Never Used(RC).000 Others. * Wealth Index Poor(RC).000 Middle.079 Rich.560 * Currently Breastfeeding No(RC).000 Yes 3.73 ** Reading Newspaper Not at all(rc).000 At all 0.8 * Listening Radio Not at all(rc).000 At all 0.669 Watching TV Not at all(rc).000 At all 0.84 ** Notes: * p<0.00, ** p<0.05 & RC=Reference Category Ever use of any methods is another important and highly significant factor influencing fertility of the respondents. The logistic co-efficient indicated that the highest occurrence of fertility who was among not used any methods. It appears that the used any methods women are. times more likely to fertility than the not used any methods (Table 3). Mass media can play a strong role by creating awareness about the fertility related complications and the bad effect of these complications in the future health of mothers and their new born babies. The result showed that the respondents who listening radio, reading newspaper and watched TV were more awareness on fertility than that of the women who did not watch. The result also showed the highly significant (statistically) effect for fertility. MEASURING THE WORTH OF THE MODEL There are various statistical that have been proposed for assessing the worth of a logistic regression model, analogous to those that are used in linear regression. We examine two of the two of the proposed statistics as follows: R in Logistic Regression: The worth of the linear regression model can be determined by using R but R computed as in linear regression should not be used in logistic regression at least not when the possible values of Y are zero and one. It is evident that R can be dropped considerably for every misfitted point, so R can be less than 0.9 even for near perfect fitting. Cox and Wermuth (99) also conclude that R should not be used when Y has only two possible values and show that frequently R = 0. when good models are used. Various alternative forms of R have been proposed for the binomial logit model. Maddala ( 83) and Magee ( 90) proposed using L(0) R ( ˆ L ) n () With L(0) denoting the likelihood for the null model (i.e., with no regressors) and L() representing the likelihood function that would result when replaces in the following equation g(y Y..,Y n ) = n Y i ( ) Y i i i i P P () Essentially the same expression expects that n was misprinted as, was given by Cox and Snell n ( 89). [Equation () is motivated by the form of the likelihood ratio test for testing the fitted model against the null model. It can be shown that R as defined in linear regression is equivalent to the right hand side of equation (). Hence, this is a natural form for R in logistic regression]. Since the likelihood function L() is a product of probabilities it follows that the value of the function must be less than. Thus, the maximum possible value for R defined by equation () is max R = -{L(0)} /n. In linear regression Yˆ Y is used

Human Fertility Behaviors in Bangladesh: A Multivariate Statistical Analysis 5 for the null model. Similarly, in logistic regression we would have p ˆ for the null model with denoting the percentage of s in the data set. It follows n n n that max R ( ). For example if = 0.5 then max R = 0.75. This is the largest possible value of R defined by equation (). When the data are quite sparse, the maximum possible value will be close to zero. Therefore, Nagaelkerke (99) suggests that R be used with R R max R From the above fitted model the Cox and Snell R = 0.85 and Nagelkerke R = 0.379. It is observed that when the value of R exceeds 0.5 the data fit the binary logistic regression model well. Therefore, the model can be used for the significance prediction about the index of fertility in Bangladesh.. Correct Classification Rate (CCR): We may criticize any statistic that is a function of the P when ˆi Y is binary. Each P and its closeness to Y ˆi i depends on more than the worth of the model. If our objective is to predict whether a subject will or will not have the attribute of interest a more meaningful measure of the worth of the model would be the percentage of subjects in the data set that are classified correctly. Accordingly, we will use the correct classification rate (CCR) as a measure of the fit of the model. In order to find the CCR we have the following table (Table 4). TABLE 4 Observed classification table a,b Index of fertility Predicted Percentage Disfavorable Favorable correct Observed Disfavorable 0 47 0 Favorable 0 9849 00 Overall 89.6 percentage a. Constants include in the models b. The cute value of.500 If we use 0.5 as the threshold or cut value we have from Table 5 CCR=90.7. Since a model that affords better classification should be judged superior by a goodness of fit test that indirectly assesses the classification performance of the model. Through classification performance we conclude that our fitted model may be used for prediction. TABLE 5 Predicted classification table a Index of fertility Predicted Percentage Disfavorable Favorable correct Observed Disfavorable 97 850 5.9 Favorable 77 967 98. Overall 90.7 percentage a. The cute value of.500 CONCLUSION It can be safely concluded from the study that fertility control measures need urgent attention in the Bangladesh. Due to absence of well-recognized structure of the health care delivery, fertility indicators of these areas are higher than the national and state level statistics. Awareness programs and family welfare service delivery should focus on urban slum areas to control the fertility. Marriage is a universal institution to enter into marriage relationship intended to meet sexual and reproductive needs, to control sexual drive and to adapt to environment in Bangladesh, as are in many other cultures around the world. In particular, prefer more early age at first marriage for both male and female and more age differences in marital relationship compared to urban community and affluent class who support delayed age at first marriage for both male and female. The above discussion leads to the conclusion that education is one of the most viable means for enhancing the status of women raising the education. Therefore, even more vigorous attempts should be made to keep the girls in school for an extended period. Along with formal education, women must have access to informal education. One dimension of informal education is that women should be made aware of the risks and consequences of fertility. The mass media can play an effective role in this regard. A social mobilization program through the same media might also be an effective way to change the orthodox religious and cultural values regarding fertility. Public opinion may also be sought in coming to a consensus regarding a clear legal fertility. Moreover, fertility should be entered into with the

6 Md. Mosharaf Hossain & J. A. M. Shoquiluer Rahman full consent of the intending couples. Special efforts should be made to provide paid employment for women in suitable places. In respect of the education of children, efforts should be made at multiple levels to ensure that there is no discrimination according to sex. The evidence presented above leaves no doubt regarding the important role played by working status of women. Working status of women has come out to be the stronger determinant in rising age at marriage of Bangladeshi women. This indicates that the women who earn cash for work have a depressing affect on fertility. It is also observed that fertility decreases with increased female literacy rate and female economic activity rate. Women participation on employment should be increased and encouraged, hence, increase the working status of Bangladeshi women and age at marriage to reduce fertility. REFERENCES CITED Agarwala, S. N. 967. Effect of a rise in female age at marriage on birth rate in India. In: Proceedings of the World Population Conference, Belgrade. Basu, S. K., G. K. Kshatriya, and A. Jindal 988. Fertility and mortality differentials among the tribal population groups of Bastar district Madhya Pradesh. Human Biology, 60:407-46. Bhasin, M. K. and Shampa Nag 00. A demographic profile of Jammu and Kashmir: Estimates, trends and differential in fertility behaviour. Journal of Human Ecology, 3: 57-. Bhasin, V. 990. Habitat, Habitation and Health in the Himalayas. Kamla-Raj Enterprises: Delhi. Bhuyan, K. C. and M.U. Ahmed 984. Fertility and family planning practices in rural Bangladesh. The Journal of Family Welfare, 30(3): 57-70. Choudhury, R.H. 984. The influence of female education, labor force participation, and age at marriage on fertility behavior in Bangladesh. Social Biology, 3: 59-73. CIA 0. CIA World Fact book 0. U.S. Department of State. Area FF Handbook of the US Library of Congress. Cox, D. R. 970. Analysis of Binary Data. Chapman and Hall Ltd.: London. Cox, D. R. and E. J. Snell 989. The Analysis of Binary Data. nd edition. Chapman and Hall: London. Cox, D. R. and N. Wermuth 99. Response models for mixed binary and quantitative variables. Biometrika, 79: 44-46. Durch, J. A. 980. Nuptiality Patterns in Developing Countries - Implications of Fertility. Population Reference Bureau: Washington. Elamin, M. A. and K. C. Bhuyan 999. Differential fertility in north eastern Libya. The Journal of Family Welfare, 45(): -. Fox, J. 984. Linear Statistical Models and Related Methods. Wiley and Sons: New York. Lee, E. T. 980. Statistical methods for survival data analysis. Life-time Learning Publications: Belmont, CA. Maddala, G. S. 983. Limited-dependent and qualitative variables in economics, 57-9. Cambridge University Press: New York. Magee, L. 990. R square measures based on wald and likelihood ratio joint significance tests. The American Statistician, 44:50-53. Maudlin, W. P. and B. Berelson 978. Conditions of fertility decline in developing countries, 965-975. Studies in Family Planning, 9: 89-45. Mukherjee, B. N. 975. Status of woman as related to family planning. Journal of Population Research, :5-33. Nagelkerke, N. 99. A note on a general definition of the coefficient of determination. Biometrika, 78(3):69 69. Pandey, G. D. and P. P. Talwar 987. Some aspects of marriage and fertility in rural Uttar Pradesh. Demography India, 6:30-30. Pandey, P. L., D. C. Jain, G. D. Pandey, R. Choubey and R. S. Tiwary 000. Some aspects of social factors affecting fertility behaviour of Gond Women. Man in India, 80(3&4): 5-58. Thompson, W. S. and D. T. Lewis 965. Population Problems. McGraw Hill Publishing Co.: New Delhi. Yadav, S. S. and V. S. Badari 997. Age at effective marriage and fertility: An analysis of data for North Kanara. The Journal of Family Welfare, 43(3): 6-66.