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What are the Proximate Determinants of Fertility?


❶The observation was nearly universal that industrialization was causing this decline, with those who were in more industrial settings i. Because levels of primary sterility in Kenya are often close to 3 percent, the index has little explanatory value in this analysis.


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Bongaarts’ aggregate model of the proximate determinants of fertility

Because amenorrhea, which is determined by the length of breastfeeding, decreased by almost 1 month over the same period, this increase in breastfeeding may be more an artifact of the data than a reflection of what actually occurred. By —, the length of breastfeeding was approximately the same by type of residence and did not vary substantially across educational group. Length of amenorrhea and abstinence was longer in rural areas and among the least educated women.

The effect of postpartum amenorrhea and abstinence on fertility is measured by C i , the index of postpartum infecundability. The working group is not aware of any studies assessing the validity of this increase in breastfeeding or linking it to possible causal factors, such as breastfeeding campaigns or the price of food for children. Because C i is calculated from the mean length of postpartum amenorrhea or abstinence, whichever is longer see Appendix , the possible errors in the reported length of breastfeeding do not directly affect the estimate of C 1.

However, if the increase in breastfeeding is real and is not reflected in an increase in the length of amenorrhea due to misreporting , Ci will be overestimated. Primary sterility, or the inability of a woman to bear a child for biological reasons, has historically been high in sub-Saharan Africa, particularly in Central Africa Frank, ; Bongaarts et al.

In societies that value large families, such levels of sterility prevent some women who would like to bear children from doing so and lower the average level of fertility. In this analysis, primary sterility is measured by the percentage of ever-married women age 40 to 49 who are childless. However, there were substantial differentials among subgroups of the population.

Urban areas demonstrated a very high level of primary sterility, with 9. Primary sterility was higher among those women with no education than among those with some education. Nairobi and Coast provinces demonstrated fairly high levels of sterility, 8. By —, the national level of primary sterility decreased to 2. Sterility remained higher among urban women than rural women and among those women with no education than women with 1 to 7 years of education.

Primary sterility was still highest in Nairobi; however, levels dropped dramatically to 2. Levels rose in Eastern by 1. Primary sterility is measured by the I p index. Its calculation is based on a 3 percent standard rate of childlessness. When the rate of childlessness exceeds 3 percent, I p is less than 1, indicating that fertility is inhibited.

If the rate of childlessness is less than 3 percent, I p is greater than 1, indicating that primary sterility is lower than expected in a developing country. Because levels of primary sterility in Kenya are often close to 3 percent, the index has little explanatory value in this analysis. Accordingly, the index is omitted from many of the figures; however, it is retained in the tables.

Furthermore, it is worth emphasizing that the numbers used to estimate primary sterility were calculated from very small samples. Therefore, the reliability of the numbers, particularly for subgroups of the population, is questionable. There are very few data on abortion in Kenya, partly because the procedure is illegal except when a woman's life is in danger Lema, However, some hospital data are available. In a study by Rogo , it was noted that Kenyatta National Hospital in Nairobi treated 2, to 3, women for abortion complications in the late s and early s, and about 30 to 60 women a day, or 10, women a year, in Jacobson, Case histories of primarily low-income urban women gathered in a study by Baker and Khasiani implied that abortion is fairly common, particularly among single and unemployed women.

Robinson and Harbison used the data from Kenyatta National Hospital in and the district hospitals to estimate an abortion rate of 25 procedures per 1, women per year, assuming that for every woman admitted to a hospital for abortion complications, four other women attempted an abortion. In this analysis, we use this estimate for C a , the index of abortion. Unfortunately, not enough data were available to attempt to esti-. Because of the unreliability of these estimates due to very small sample sizes, we have not calculated I p for individual districts.

We have used the province-level I p as a proxy for each district. Figures to summarize the effects of marriage patterns, contraception, and postpartum infecundability on fertility for subgroups of the population. The effect of marriage patterns on fertility is summarized in the index C m. In —, C m was. Marriage patterns reduced fertility by about 0.

However, the change in C m is not substantial or significant reducing TF by only 0. In fact, it has been estimated that age at first birth is lower than age at first marriage Westoff, a.

A similar pattern occurs across most of the subgroups: Changing marriage patterns have resulted in slightly lower fertility. Among the three age. The results in the figures are presented using a logarithmic scale; thus, fertility reductions due to postpartum infecundability, contraceptive use, and nuptiality appear small because they are compressed.

To express the effects of each index in births per woman, the following calculations are used Bongaarts, Care should be taken in interpreting these effects, expressed in births per woman, because the results depend on the order in which the effects are calculated.

However, because Bongaarts et al. Because of later entry into union for urban and well-educated women, C m had the strongest effect in inhibiting fertility among these two groups. The urban-rural differential is not as pronounced in the KDHS as in the KFS, which suggests that marriage patterns are becoming more similar for women across types of residence.

Differentials have also narrowed among educational groups. They had their weakest effect in Eastern, Nyanza, and Western provinces. In —, marriage patterns inhibited fertility most substantially in Nairobi, Eastern, and Central, in that order. At the district level, in —, Kirinyaga and Meru had the lowest C m. The dramatic increase in contraceptive use between — and — is reflected in a decrease in C c from.

The greater fertility-inhibiting effect of C c is reflected across all subgroups. Although there was little variation in the effect of contraception among the three age groups in —, differentials increased by , when contraceptive use had its greatest effect on fertility among the middle and late age groups. As expected, contraception inhibits fertility most among urban and well-educated women.

Although differentials did not change by type of residence between the two surveys, there was greater variation in C c among educational groups in — In — among the provinces, contraception had its greatest fertility-inhibiting effect in Central, Eastern, and Nairobi in that order, reflecting the highest contraceptive prevalence rates.

These same three provinces had the lowest C c in —, but only Nairobi exhibited a very strong index. It should be noted that the formula for C c see the appendix at end of chapter for the exact calculation has not been modified for each age group. At issue is the 1. South Nyanza, Siaya, Bungoma, and Kilifi showed relatively weak contraceptive use effects. The index of postpartum infecundability C i had the greatest effect of all the indices at the national level.

C i remained relatively unchanged between the two surveys. Furthermore, little change in C i is evident across subgroups, except for a weakening increase in C i in Coast and Eastern provinces. Because of longer periods of postpartum amenorrhea and abstinence among rural and little-educated women, C i had its greatest fertility-inhibiting effect in these two groups at both times.

Its effect consistently weakens with increased education. At both times, Western and Rift Valley provinces had the lowest C i. Siaya, South Nyanza, and Kisii, all in Nyanza Province, had the longest nonsusceptible period of the districts, which is reflected in very low C i. Primary sterility generally had little effect on fertility across subgroups in Kenya from — to — However, it did have an effect on the fertility of urban women, particularly in the KFS.

Nairobi and Coast showed an effect in —, but this effect was eliminated by —, reflecting either a drop in rates of primary sterility due to improved medical care or sample sizes that were too small to yield reliable estimates.

In looking at the national-level indices from the KDHS, the most important fertility-suppressing index is postpartum infecundability, followed by contraception, and then marriage. Abortion and primary sterility had limited effects.

Results from the — KFS also indicate that postpartum infecundability was the most important fertility-inhibiting variable at the national level. Marriage patterns C m followed in significance in the earlier period, with contraception having a relatively minor effect. Moreover, there are regional disparities in fertility in the country. Regardless, no detailed study on fertility that had policy implication in the context of current decentralization for such high fertility regions was recently done in Ethiopia.

Various individual and household background characteristics of women influenced the level of fertility which required systematic assessment. The study area is also located in one of the densely populated and resource constrained parts of the country. Frequent food shortages, land degradation and population pressure lead residents to migrate and face high mortality of children under the age of five years [ 7 - 9 ].

This also gives an additional impetus to assess the effects of migration and childhood mortality on fertility in the study area. Therefore, the main purposes of this study are to assess determinants of fertility in rural Ethiopia, characterized by high child mortality and mobility resulting from population pressure, frequent episodes of drought and pestilence.

This study was conducted in Butajira demographic surveillance system DSS started with 10 villages 9 rural and 1 urban sampled according to probability proportional to size technique from 82 rural and 4 urban villages [ 10 ].

Residents of the study region varied in type of residential ecology, social, cultural, environmental, reproductive health and economic characteristics [ 11 ]. Active resident women in the reproductive age group recruited from the Butajira DSS database were interviewed during October-December The number of women in the reproductive age group living in the Butajira Demographic Surveillance Area DSA at the time of the survey was 11, A structured Demographic and Health Survey DHS type maternity history questionnaire was developed in English and translated into the local language of the respondents, and then back translated to English by an independent person.

The questionnaire has been pilot tested in a different area prior to this study. Twenty clinical nurses and 5 supervisors all with a Bachelor degrees were recruited as data collectors and supervisors, respectively.

Clinical nurses were recruited because advice on family planning use was part of the ethical consideration. Various data quality assurance mechanisms including using a standard data collection tool, recruitment of qualified female field staffs, intensive supervision, and mechanisms to minimize information contamination were put in place. Professional bias was over emphasized during the training prior to data collection to minimize it.

Data were cleaned by reconciling inconsistencies. Moreover, Poisson regression [ 12 , 13 ] Incidence Rate Ratio IRR with 95 percent confidence interval CI was used to assess the association of various maternal and household characteristics with fertility. We checked that all assumptions of Poisson regression were fulfilled. Total children ever born to women in the reproductive age group which is a count data is considered as the outcome variable for this study.

The overall significance of each covariate was first checked and those turned statistically significant were included in the bi-variate and multivariate Poisson regression model to compute crude and adjusted IRR. The reference category for each of the factors included in the model was selected based on a prior knowledge that women in this category had smaller fertility compared to the rest of the categories except for the case of household livelihood.

Support letters were obtained from the districts, in which the study was conducted, through the Butajira DSS which hosted the study. Oral consent was also obtained from each study participant. The mean age of first marriage of study participants was estimated to be Study participants were fairly distributed in different residential ecological zones. Having large household size appears to be an accepted norm as nearly 59 percent of study participants were living in households that had more than four family members.

The average household size was around 5. More than 65 percent of women in this study belonged to households whose main livelihood was farming. Sixty five percent of the study participants were born within surveillance villages. About 28 percent of the study participants lived in food-insecure households. About 43 percent of the interviewed women had incidence of child death. Sixty two percent of women did not have sex preference for their children.

The mean children ever born to women in the reproductive age group was found to be 4. On the other hand, total fertility rate TFR was estimated to be 5.

The age specific fertility rate revealed a typical developing country pattern shown elsewhere. The parity progression ratio, the conditional probability of having the next parity given that the women had already a certain parity level, revealed that women of parity four had Educational status of women had also been consistently and significantly found to be negatively associated with fertility.

Women who had never been into any formal education had 1. Residential ecology composed of altitude and residence type in this study. The DSA comprised of lowland below m , midland m and highland more than meters above sea level. Rural areas covered lowland and highland areas while Butajira fall in midland area. Residential ecology was significantly associated with fertility although the direction of association was changed when other covariates were included.

Women resided in lowland rural Butajira had 1. However, when other factors are included, women who lived in lowland rural Butajira had 12 percent lower fertility compared to urbanites. No fertility difference was observed between urban and highland rural Butajira when other factors were included. On the other hand, women who were members of a larger household five plus had about 2 times higher fertility compared to those who belonged to smaller households after other factors were added into the model.

Fertility among women whose households' main source of income was trade or service had 14 percent lower fertility compared to their counterparts whose household livelihood was farming after other factors were put into the model.

On the other hand, women belonged to families whose household income was from the civil service had lower fertility compared to those earning their household income from farming although the statistical significance vanished when we control for other important variables. Meanwhile, the study was conducted in a drought prone area [ 7 ].

Women who were members of a food-insecure household had 6 percent higher fertility as compared to their counterparts in food secure households. The fertility of in-migrant women to the demographic surveillance area was lower than those who were born in the DSA although the association was statistically not significant when the effect of other vital variables were controlled.

Women who had lost at least one of their children had about 1. Fertility was about 9 percent higher in women who did not know the time at which women could be pregnant if they had sex. Similarly women who had no sex preference to their children had about 9 percent higher fertility compared to those with sex preference after including other significant covariates. Total fertility and marital fertility rates of 5. The fertility level is still one of the highest.

This could be attributed to the credence of the wider community to large family size norm as children assisted households in subsistence farming and petty trade.

Though disparities were observed across major regions of the world, children were considered as assets to their parents when they get older as shown in a study using the Demographic and Health Survey in 43 countries [ 15 ]. This posit was further supported by the statistically-significant finding of higher fertility among women who were members of larger households compared to those who belonged to smaller sized households less or equal to 4 members in this study.

In this study, household constituted individuals regardless of their blood relations that live in one or more houses with the same cooking arrangement. Most members of the household were nuclear family members. They are called proximate as they are nearest to the event of fertility. It is possible to study fertility differentials among various populations or trends in fertility levels of any country over a period of time by studying the variations in one or more of the proximate variables.

The proximate determinants of fertility can be classified in two groups, viz. It is obvious that a girl becomes capable of bearing children only after menarche the first menstruation. Thus the menarche marks the beginning of the reproductive span or period and the menopause marks the end of the reproductive period.

Since in Indian and most other societies, socially sanctioned child-bearing is limited only to married women, the marriage of the girl is the starting point of her reproductive period and the disruption of marriage either by death of the husband Or divorce or separation or menopause onset of permanent sterility , whichever is earlier, is the end point of her reproductive span.


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Bongaarts’ aggregate model of the proximate determinants of fertility. Bongaarts (, ) and Bongaarts and Potter () refined Davis and Blake’s framework into 7 important factors, which were termed as the proximate determinants of fertility, to understand .

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The proximate determinants of fertility can be classified in two groups, viz., (1) those influencing the length of the reproductive span and (2) those influencing the rate of child-bearing within the reproductive.

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Read chapter 5 Proximate Determinants of Fertility: This detailed examination of recent trends in fertility and mortality considers the links between thos. Use this quiz/worksheet as an instrument to cement your awareness of the proximate determinants of fertility. If you want a hard copy assessment.

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iv Determinants and Consequences of High Fertility | A Synopsis of the Evidence T his report was prepared by John B. Caster-line (Robert T. Lazarus Professor in Popu-. THE PROXIMATE DETERMINANTS DURING THE FERTILITY TRANSITION Jean-Pierre Guengant* INTRODUCTION Fertility has declined very markedly in the majority of developing countries over the past thirty to.