Fertility decline has occurred in Iran since the second half of the 1980’s, which has changed the perceptions and desires of Iranian families toward childbearing. Accordingly, Iran is now settled among the low-fertility countries (1).  In 1986,average fertility rate was six per reproductive woman today. However, average woman has fewer than two children today, which is less than the required replacement level fertility level (2). The fertility transition in Iran shows that although family planning programs have been effective in accelerating the dramatic decline in the fertility rate, fertility behaviors should always be considered in the analysis of fertility trends (3). It is worth mentioning that fertility and childbearing motivations, as the main determinants of the demand for childbearing, play important roles in fertility-related theories (4-11). Childbearing desires (CD) and ideal number of children (INC) are two most important childbearing preference dimensions (6, 10). 

The accuracy of the classification tree for this model can be calculated by Equation 1.

Classification accuracy equals to 0.61 means that DTC of 61% of women was classified correctly. In other words, the misclassifications of this model was equal to 39% (23).

To fit CART algorithm to data sets, data were divided into two different groups of training and learning data. in fact, the training data was used for fitting the model and learning data for confirming the validity of the model. the validity of the fitted model is confirmed when the risks of these two data groups are close to each other (29). The risk is a measure of the predictive accuracy of the tree. For categorical dependent variables, the risk estimate is the proportion of cases classified incorrectly after adjustment for prior probabilities and misclassification costs.

Table 3 tabulated the risks and standard errors of classification tree which are calculated based on training and learning data.

As can be seen in Table 3, these values are almost equal indicating the validity of classification models proposed by classification tree in Figure 1. According to Figure 1, the CART algorithm entered all of the investigated variables in the classification tree as nodes. Province has been placed in the root of the classification tree as the most influential variable on classifying DTC. The following 9 rules were extracted from Figure 1.

1. Women in provinces with TFR less than 2, living in rural area aged younger than 30, had demand for a third child.
2. Women in provinces with TFR<2, living in rural area with age range of 30-49 years with negative opinion, had no demand for a third child.

1. Women in provinces with TFR<2, living in rural area with age range of  30-49 years with positive or neutral opinions, marriage age of less than 20 years, had no demand for a third child.
2. Women in provinces with TFR<2, living in urban area with negative and neutral opinions, had no demand for a third child.
3. Women in provinces with TFR<2, living in urban area with the age range of 30-49 years, positive opinion, educational level of high school or higher above, had no demand for a third child in contrast with less educated ones.
4. Women in provinces with TFR<2, living in urban area aged younger than 30 with positive opinion and marital age of less than 20 years had no demand for a third child in contrast with married women in age the age range of 20-29.
5. All the rural women in provinces with TFR>2 had demand for a third child.
6. Women in provinces with TFR>2, living in urban area with positive opinion, had demand for a third child.

Women in provinces with TFR>2, living in urban area with high school degree or higher, as well as negative and neutral opinions had no demand for a third child in contrast with women who had a low level of literacy (e.g. illiterate, primary and secondary school educational levels).

Discussion

In this study, ‘demand’ has been used to measure the individual’s desire for having another child. Various terms, such as tendency, desire, or willingness have been used in different studies to examine this variable. Fertility could result in having this demand (17-20). Some studies addressed the unwillingness to have more children (17, 19).

According to the results of this study, educational level had a significant influence on DTC, which was in line with the obtained results of previous studies (18, 20, 25). Nowadays, the increase in the women’s educational level and their tendency to benefit this education enhances their willingness to participate in social activities, such as working outside home. Therefore, they prefer to work outside their home due to economic reasons or social situations. Accordingly, this situation makes having children for most of women a deterrent factor, which can result in lower possibility of giving birth to more children. Factors, such as entering the university, changing individual beliefs through heightening educational level, and shaping modern attitudes, directly increase the marriage age, which indirectly lead to delay in childbearing age and fertility rates (18). Moreover, the marriage age is one of the factors affecting fertility according to the results of this study. Increase in the average age of marriage is always reported as one of the major reasons of low fertility (3).

Due to the increase in the fertility age of women in Iran, it is necessary to develop childbearing culture at the right time for couples. The reason for this is that women and couples are worried about childbearing at old ages. In the current study, women’s marriage age also influenced DTC. In a study conducted by Kazemipour in 2014 (2), there was a negative relationships between the age and tendency to have a child, meaning among married women within the age range of 15-49 years. This means that by increasing the marriage age of youth in threshold of marriage, their desired number of children decreased. Saadati et al. (2017) also concluded that by preparing the condition for on-time marriage, the desired number of children could be reachable (22). This result is similar to the obtained results of the study conducted by Abbasi-Shavazi and Asgari-Nadushan (2005) (26). They mentioned that women who married in the higher ages had fewer INC compared to the others.

Likewise, Abbasi-Shavazi and Khajesalehi (2013), Hossaini and Baigi (2014), and Hejazi (2013) reported women’s age as an influential factor on DTC (17-19). The increase in women’s age decreases their desire of having more children. Saadati et al. (2017) indicated that if the socio-economic condition for younger women changes according to their needs, they will have at least 2 children (22). The unwillingness for older women could be caused due to the decrease in their ability to give birth and bring up their child.

On the basis of this study, opinion under the influence of cultural, economic, and social variables could also affect the decrease in the women’s desires. Women who had negative attitude toward childbearing were less likely to have another child in contrast with those with positive opinion, which was in the same vein with the findings of the studies conducted by Hossaini and Beigi, as well as Beigi (18, 28). Negative opinions of women included their fear of being blamed by others for having more children, less happy moments with the presence of a child, high cost of childbearing, which could decrease their willingness to have more children. Moreover, some women considered children as barriers to their occupational and educational status.

The place of residence could also have a significant impact on DTC, which was confirmed by previous studies (14, 25). Women living in rural areas compared to those in urban areas had the desire to bear more children. A study conducted by Saadati and Bagheri (2016 a, b) concluded that dividing provinces according to TFR could result in different results in different divisions (23, 25). The findings of the current study was also indicative of the fact that women in provinces with lower level of TFR were not as interested as those in provinces with TFR>2 to have a third child.

This study aimed to analyze the influencing factors on DTC, an indicator of behavior fertility in all provinces, through CART method. To the best knowledge of researchers, few studies investigated DTC and the effects of different variables on that. It is worth mentioning that can be applied in future studies since the results of the current study were obtained based on a large representative sample and the data were analyzed by the most applicable classification method, named CART. Although this study provided useful findings, nonetheless the limitations must be considered. The data from DTC did not compare men and women, and was focused only on women. In order to provide more generalizable results, the researcher intends to draw from both men and women. The outcomes of the study can pave the way for policymakers to design and adopt fertility planning programs.

 Conclusion

Based on the obtained results of this study, women’s place of residence and opinion play a significant role on their DTC. However, in provinces with TFR lower than two, women’s educational level, age, and marriage age affected their DTC in comparison with women in other areas.

Fertility decline is not desirable for the population growth of any society. Therefore, it is important to adopt programs, which encourage fertility and increase the fertility rate to the replacement level. In doing so, there is a desperate need to analyze and examine the factors which affect fertility behaviors. Without considering these influential factors, fertility planning programs cannot be as successful as expected.

 Acknowledgements

This article is extracted from a survey under the title of “Mining Demographic Data by Decision Tree” which is supported by National Population Studies and Comprehensive Management Institute in 2014 (No. 20/15283).

 Conflicts of interest

The authors declare no conflicts of interest.