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What are the Proximate Determinants of Fertility?


❶At issue is the 1. We checked that all assumptions of Poisson regression were fulfilled.


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Bongaarts’ aggregate model of the proximate determinants of fertility

A DHS maternity history questionnaire was administered on participants. Data quality was assured besides ethical clearance.

Poisson regression crude and adjusted Incidence Rate Ratio with 95 Confidence Interval were used to identify determinants of fertility.

Delayed marriage, higher education, smaller family, absence of child death experience and living in food-secured households were associated with small number of children. Fertility was significantly higher among women with no child sex preference. However, migration status of women was not statistically significant.

Policy makers should focus on hoisting women secondary school enrollment and age at first marriage. The community should also be made aware on the negative impact of fertility on household economy, environmental degradation and the country's socio-economic development at large.

The three components of population change i. In countries at the second stage of demographic transition, mortality reduction is followed by fertility decline [ 1 ]. However, drop in fertility is not yet witnessed in the general population of sub-Saharan Africa although it has been observed in some metropolitan areas and selected communities in the sub continent [ 2 - 4 ].

Ethiopia has never been unique in this regard as there were only half a child cut off between and from a total fertility rate of 5.

Fertility is higher in rural compared to urban areas in the country. Nevertheless, below replacement level fertility of 1. The total fertility rate was however over 6 children per woman in rural Ethiopia. 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.

The fact that women of parity 4 had more than 79 percent chance to have the 5 th parity augmented the deep rooted culture of larger family size that might have been supported in the study community in the foreseeable future.

Thus, the total-fertility-rate goal stipulated in the Ethiopian population policy is far from reach [ 16 ]. Women that married in their teens had a significantly higher fertility compared to those married after they celebrated their 20 th birthday. Moreover, this study revealed that current contraceptive prevalence among women in the reproductive age group and married women were 15 and 25 percent, respectively figure not shown.

Besides women who marry early in life may have an increased risk of having many children, in particular, if they started childbirth before the age of 20 years. On the contrary, several studies [ 16 , 17 ] have shown that postponement of first childbirth to later ages leads to fertility reductions since women would have fewer years of reproduction window which may introduce parity specific controls even after the initiation of child birth.

On the other hand, women who had many years of education had significantly lower fertility as compared to those who had never been enrolled into any formal education system. This corroborates with similar studies [ 13 , 17 - 19 ] and may be attributable to the postponement of childbirth due to longer schooling. Educated women might be more worried to have many children if their area of usual residence had been stricken by frequent food shortage [ 7 ].

Nonetheless, a study among Sidamas in Southern Ethiopia indicated that fertility was higher among women with primary level of education compared to those who never attended any formal education. Higher educational level of women gives an opportunity of social and economic empowerments. Thus, able women might feel that they could take care of many children and opted for large family size. This is consistent with the claim by some researchers that increased family income leads to increased fertility when family planning use is low [ 17 ].

Education might also have impacts to bring about change in the knowledge and attitude towards low fertility. By the same token, women who did not know the time at which they could be pregnant had higher fertility as compared to those who knew it.

Disparities in level of fertility between urban and rural communities in this study population were similar to the finding in Gondar [ 18 ] that could be attributed to differences in contraceptive prevalence and age at first marriages between urbanites and rural residents in Ethiopia since women in urban areas had better access to media, general knowledge and services.

It was however difficult to document reasons for the change in the direction of associations between residence ecology type and fertility when other factors were controlled. 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.

Again in India, a small percentage of girls is married before the onset of menarche. In such a situation, the onset of menarche is the beginning of the reproductive period. Within the reproductive span, the married and fecund woman reproduces at a rate inversely related to the average duration of the birth interval.

High fertility is associated with short birth intervals and low fertility is associated with long birth intervals.


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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.