Trends in fertility preference implementation among selected Eastern African countries

Introduction

Over the last decade, there has been considerable debate among scholars on fertility transition in Africa. Two conclusions emerge: slow pace of decline (Bongaarts, 2017; Bongaarts & Casterline, 2013) because of weak facilitating social programs (Otieno et al., 2016) and high demand for large families amidst weak family planning programs (Bongaarts & Casterline, 2013). Accelerated fertility decline in Sub-Saharan Africa will occur if there is both substantial decline in desired fertility and increased level of preference implementation (Bongaarts, 2017; Bongaarts & Casterline, 2013).

Despite these conclusions, there are also emergent exceptions, such as Rwanda, Ethiopia, Malawi (Bongaarts, 2017; Mahy & Gupta, 2002). In a seminal workshop organized by Committee on Population of the National Academy of Sciences in 2015, there were suggestions that further search for explanations of fertility transition may lie in examinations of specific historical contexts of each population (Casterline & Han, 2017). Our motivation for this study lies in this context. First, the East African countries share some common policy framework. Secondly, Rwanda and Kenya appear exceptional in the drive towards accelerating further fertility decline.

Kenyan fertility began to rapidly decline in the 1980s, followed by a stall (1998–2003) and then another phase of decline (2003–2014) recently (KNBS, 2015). Tanzania progressed slowly in decline followed by stall mainly in rural areas (Ezeh et al., 2009; Garenne, 2014) and then further decline within similar timelines as Kenya (Otieno et al., 2016). Uganda has generally been regarded to still be at the pre-transition stage of fertility decline, recently exhibiting some modest declines (Ezeh et al., 2009). Rwanda experienced rapid decline from 2005–2015 (Dhillon & Phillips, 2015; May, 2017) because of the government’s management of the economy and provision of social and health services, including family planning, which are exceptional by regional standards (Dhillon & Phillips, 2015; May, 2017). On the other hand Muhoza et al. (2014) revealed remarkable differences in desired and excess fertility between the four East African countries and between certain communities within these countries.

The main reason for focus on these countries is that any fertility change in one country may have implications in the neighboring country, because neighbouring regions share common dynamics, including language and cultural traits that permit shared flow of ideas to eventually spread new models of behavior. We analyze trends in two specific features that scholars have indicated will slow or increase in fertility decline. First, trends in fertility preferences among women and secondly, the extent to which women have been able to implement their fertility preferences during the course of fertility decline. Because of heterogeneity in fertility change within countries, we focus on trends by sub-national regions and social class stratification, since understanding the differences in fertility according to socioeconomic status and how these differences evolved over the fertility transition period is of great importance in understanding fertility decline. Conclusions about fertility change have been based on mostly cross-national comparisons based on national data but there has been less attention to sub national differentiation. It has been observed that enormous heterogeneity exists in Kenya, where the wealthy and higher educated people have fertility desires close to replacement level, regardless of religion, while poor, uneducated people, particularly those in Muslim communities, have virtually uncontrolled fertility (Muhoza et al., 2014). On the other hand, in Rwanda the poor, uneducated people have the same desired fertility as their wealthy, educated compatriots, regardless of their religion.

Conceptual framework

This study was guided by Bongaarts (1993) modification of the supply-demand framework for fertility analysis developed by Easterlin (1975) and Easterlin & Crimmins (1985). According to Bongaarts, Easterlin’s economic approach is a model of behavioral and biological factors affecting fertility in developing countries. The model consists of three fundamental concepts: demand for children, the potential supply of children, and the momentary and psychic costs of contraception. According to the model, women whose potential supply of births exceeds demand would consider contraception, taking into consideration the costs involved while choosing suitable family planning methods (Montgomery, 1987). In this modification depicted in Figure 1, the fertility outcome measured by the total fertility rate is a function of: supply of births (natural fertility); demand for births (wanted fertility) and degree of preference implementation. The latter in turn is dependent on cost of fertility regulation and cost of unwanted childbearing. The degree of preference implementation is the net result of a decision-making process in which couples weigh the cost of fertility regulation and the cost of unwanted pregnancy.

Figure 1. Key variables and interrelations in variant of supply-demand model.

Source: Bongaarts (1993). The supply-demand framework for the determinants of fertility: An alternative implementation.

Methods

According to Bongaarts (1993), the relationship between these variables and fertility can be expressed as:

Where F_u is unwanted fertility (which can simply be expressed as F_o – F_w), and

Where F_n is total natural fertility and I_p is the degree of fertility preference implementation index. I_p ranges from 0 to 1. With full preference implementation, I_p = 1 (which implies that F_u = 0 and F = F_w) and I_p is 0 with no preference implementation (this implies a substantial level of unwanted childbearing and F = F_n). Bongaarts (1993) indicated that F_n is not the same as in total fecundity used in the Bongaarts proximate determinants but is taken to mean fertility level achieved in absence of contraception (see Bongaarts, 1993). F_u is a function of the difference between supply and demand, and the degree of preference implementation. Substitution of Equation 2 from Equation 1 yields

Where C implies is an index ranging from 0 to 1 measuring the reduction in proportional of natural fertility attributable to deliberate birth control.

Where U represents the proportion of married women who were practicing contraception at the time of survey. It is measured as the number of married women using contraceptive method to the total number of married women. Substitution of Equation 5 into Equation 4 gives an estimate of F_n, while rearranging Equation 3 gives:

Decomposition of fertility trends

When estimates of observed, wanted and natural fertility, as well as the index of implementation are available for two successive points at time t₁ and t₂ in the same population (Bongaarts, 1993), then a decomposition can be obtained to identify causes of fertility declines in specific populations (Table 1). Again, following Bongaarts’ 1993 formulation, the following variables were used.

The decline in fertility between t₁ and t₂ is simply equal to F₁ – F₂, and this difference can be expressed as a function of the mediating variables by substitution of Equation 3.

Since the emphasis here is on examining changes in fertility that result from changes in determinants, this equation can be rewritten as

Where ΔF, ΔF_w, ΔF_n and ΔI_p represent absolute changes in F, F_w, F_n and I_p, respectively, and F_w, F_n, and I_p with bars represent the average values of F_w, F_n and I_p, respectively. Equation 8 conveniently divides the observed fertility decline ΔF into three components corresponding to each of the three determinants as indicated below (Table 2)

The above formulation shows that a change in wanted or natural fertility to the observed fertility decline depends on the average level of implementation index. Similarly, the fertility effect from a given change in the index of implementation depends on the average between natural and wanted fertility (F_n – F_w) Table 1 and Table 2). The percentage contribution of each of the determinants to fertility decline can also be obtained by multiplying the ratio of change of each of the determinants to total fertility change by 100.

Data

This study utilized cross sectional secondary data gathered overtime by the Demographic and Health Surveys (DHS) collected between 1988 and 2015 for Kenya, Rwanda, Uganda and Tanzania from women of reproductive age (15 to 49 years). DHS data is highly comparable across countries and has been shown to be of high quality. DHS data often computes first the total fertility rates based on data for the three years preceding the survey for age group 15–49 expressed per woman and second the total wanted fertility rates for the three years preceding the survey for age group 15–49 expressed per woman. Total wanted fertility rate is calculated in the same way as the total fertility rate, but only including wanted births. However, an alternative method is also to utilize the formula for the proportion of women age 40–49 in union who want no more births. According to Bongaarts, F_w can be obtained from the equation:

Where F^w is the proportion of women who want more children and W_m (40–49) is the proportion of women in union aged from 40 to 49 who want no more births. Bongaarts, however, was cognizant of the fact that indeed there are limitations especially with the information on wanted fertility. The key limitations highlighted here are rationalization, involuntary and voluntary fertility limitation.

Results

Trends in Total fertility rate, wanted fertility rate and fertility preference implementation Index

Table 3, concerning the trends in total fertility rate, wanted fertility rate and fertility preference implementation index for East African countries, shows the results of the application of Bongaarts’ 1993 formula to estimate the degree of fertility preference implementation index for the four East African Countries: Kenya, Rwanda, United Republic of Tanzania and Uganda. Notably, all the countries experienced declines in both fertility and wanted fertility rates (Figure 2). However, the highest change rate in decline occurred in Uganda. Subsequently, over the years as country fertility decline trends continue, Kenya and Rwanda have consistently registered the lowest wanted fertility rates, while Tanzania has since been surpassed by Uganda, as assessed by their respective last surveys. Wanted fertility rates for Tanzania have declined very slowly since 1996. Further, Rwanda recorded the highest rate of change in the desire to have fewer births compared to Kenya while Uganda also recorded the highest wanted fertility parameter values over the same periods until recently when it caught up with Tanzania in the course of decline.

Table 3. Trends in total fertility rate, wanted fertility rate and fertility preference implementation index for East African countries.

Country	Survey year	Total fertility rate	Total wanted fertility rate	Natural fertility rate	Preference implementation index
Kenya	2014	3.9	3.1	9.6	0.87
Kenya	2008–09	4.6	3.2	8.6	0.75
Kenya	2003	4.9	3.6	8.2	0.71
Kenya	1998	4.7	3.3	7.8	0.69
Kenya	1993	5.4	3.6	8.1	0.60
Kenya	1989	6.7	4.1	9.2	0.50
Rwanda	2014–15	4.2	3.3	9.2	0.84
Rwanda	2010	4.6	3.3	9.7	0.79
Rwanda	2007–08	5.5	3.8	8.8	0.65
Rwanda	2005	6.1	4.4	7.4	0.43
Rwanda	2000	5.8	4.6	6.7	0.44
Rwanda	1992	6.2	4.7	7.9	0.53
Tanzania	2015–16	5.2	4.5	8.6	0.83
Tanzania	2010	5.4	4.6	8.3	0.78
Tanzania	2004–05	5.7	4.8	7.8	0.70
Tanzania	1999	5.6	4.8	7.6	0.70
Tanzania	1996	5.8	4.8	7.1	0.58
Tanzania	1991–92	6.2	5.4	6.9	0.48
Uganda	2011	6.2	4.4	8.9	0.60
Uganda	2006	6.7	4.7	8.8	0.52
Uganda	2000–01	6.9	5.0	9.0	0.52
Uganda	1995	6.9	5.3	8.1	0.44
Uganda	1988–89	7.4	6.2	7.8	0.24

Figure 2. Trends in fertility implementation index (I_p) since 1989.

Discussion

In this study, we focused on the sub-national regions (provinces) of East African countries, analyzing trends in fertility preferences among women and the extent to which these women have been able to implement these fertility preferences during the course of declining fertility in these countries. The main reason was that fertility change in one country may have implications in the neighboring country because neighboring regions share common language and cultural traits that permit shared flow of ideas and eventual spread of new models of behavior. In Kenya and Rwanda, the most important contributor to fertility decline by region, place of residence and socio-economic groups is the ability to implement fertility desires (I_p). The same is true for Uganda; however, Tanzania posted mixed results by different groups.

Rwanda has had the largest fertility decline due to the contributions by both changes in the wanted fertility and ability to implement fertility desires. The results highlight one core result: fertility declines faster when both wanted fertility and ability to implement desired fertility occur simultaneously, as in the case of Rwanda. Secondly, we find enormous heterogeneity in fertility change within countries. The largest heterogeneity existed in Tanzania and lowest in Rwanda. As indicated by Muhoza et al. (2014), there are not only remarkable differences in desired fertility between the four East African countries and between certain communities within these countries, but also the degree to which the different communities are able to implement their desires. These differences may reflect cultural differences in reproductive behavior, as well as effect of development and population density which create resource needs pressures. For example, for Kenya, Tanzania and Uganda, regions with high agricultural potential, high population density and the highest densities of development inputs, have high implementation indexes and the lowest desired family sizes.

The results here concur with Bongaarts & Casterline (2013), who found that although each country has a unique fertility trajectory, fertility transitions share similarities. Countries typically have high and relatively stable fertility during the pre-transitional period which comprises most of human history (Bongaarts & Casterline, 2013). Once the transition starts, the pace of fertility decline in the first one or two decades following the onset is usually faster than in later decades. In any given country, today’s level of fertility may be a function of the pre-transitional level of fertility, the timing of the onset of the fertility transition, and the pace of fertility change. As noted earlier, conventional transition theory predicts that fertility levels are inversely related to socioeconomic development indicators (Bongaarts & Casterline, 2013).

There is some caution that may be made in the estimation of I_p as evident in the North Eastern region of Kenya and some regions of Tanzania. The I_p is dependent on development of a region, but more importantly closely associated with extent of unwanted fertility and hence unmet need for contraception. A close assessment into the cultural factors reveal that Muslim dominated areas may appear to have low degree of I_p. However, this may be confounded by other factors. Zanzibar Town West region has a high I_p while Pemba south has low I_p, whereas the North Eastern region of Kenya has low levels of contraception utilization. These regions—dominated by cultural dogmas and doctrines, especially religious beliefs—exhibit rationalized feedbacks and indeed prone to even non-numeric responses (Casterline & Han, 2017). Furthermore, the level of I_p may be dependent on program reach, confirming that fertility change must be based on the concurrent change in both women’s preferences and ability to implement those preferences.

Conclusions and recommendations

It is now clear that with the rising aggregate level of the degree of fertility preference implementation index, continuous declining trends in demand for births and the subsequent increases in the contribution made either singly or jointly by the wanted fertility and the degree of fertility preference implementation index across the various categories indicates that fertility transition is certainly on course in all countries albeit at different levels. In sum, the evidence examined here shows that the pattern of fertility decline in the region is indeed unique. In addition, the relatively slow pace and level of development in the region implies that the cost of raising children has remained low compared to other developing regions and the benefits of having offspring remain substantial in the subsistence economies, which characterizes the East African countries. This is also true of a majority of the Sub-Saharan African countries in general (Bongaarts, 2017).

The dominant mediums of diffusion of knowledge and information among populations, such as the public media and urbanization, though still very low, are expected to increase their influence. The future pace of fertility decline in the region will therefore likely be slower than the pace of decline in other regions at comparable times from the transition onset, unless special interventions are undertaken (Bongaarts & Casterline, 2013). This is a result of the duration it has taken the region to get to where it is compared to the countries that went through transition in the earlier years.

There is, however, an additional policy option to accelerate fertility decline. Investing in family planning programs to provide information about and access to contraception in order to permit control of reproductive lives and avoiding unplanned pregnancies. These countries in general are characterized among the ones with low contraception (Alkema et al., 2013). The key cause of an unmet need for contraception is that contraception is either unavailable or often too costly to the consumers and sometimes the population is ignorant of the existence or usage of contraception. In addition, there are significant non-economic costs such as health concerns, social disapproval, and spousal resistance, as well as unnecessary medical barriers requiring higher-level expertise for utilization (Casterline & Sinding, 2000). The unmet need is responsible for most of the unsatisfied demand subsequently aggravating unplanned pregnancies.

These family planning programs therefore must be accompanied by rigorous, consistent sensitization and public education campaigns through media among other modes of communication, which in turn trigger demand for contraception in anticipation of lower desired family size by diffusing new ideas about the benefits of smaller families and the role of women (Bongaarts, 2017). The degree of implementation is expected to take in values between zero and one. However, there is evidence of values greater than one as well as negative values. This points to a limitation in the frontiers of knowledge with regards to this study. Due to these glaring violations further research is recommended to assess how well the fertility preference implementation index is suited to measuring fertility success.

Data availability