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Education and fertility in sub-Saharan Africa : a longitudinal perspective
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... In sub-Saharan Africa, despite high ideal fertility desire (Casterline & Agyei-Mensah, 2017) evidences suggest that of recent a large proportion of African women have fewer children than their desired children at the end of their reproductive years (Casterline & Han, 2017). This scenario can be associated with change in reproductive desire, pattern and outcome in the fertility transition brought about by impacts of education, urbanization and the mass media role with education playing dominant role among them (Garenne, 2012). ...
... Generally for the reductions in the desired number of children, increases in the use of modern contraceptive is clearly the most important, while increases in age at first marriage play only a minor role. Increasing education is relevant to use of contraceptives (Garenne, 2012) and appears to be the primary driver of later age at first marriage. ...
... Education, an important and amenable policy variable in SSA, has rather a complex and ultimate negative relationship with fertility modifiable through a set of proximate determinants of fertility 13 . The benefits of education on fertility reduction are well articulated including exposing women to ideas of, and means to birth control or family planning 12,14,15 . However, accelerating fertility transition for SSA countries remains a challenge due to a significant proportion of illiterate women 14,16 . ...
... The Bongaarts framework was applied to quantify the impact on fertility of each proximate determinant (contraception, postpartum infecundability, sexual activity, and abortion) at national and subpopulation variation by level of education-a proxy for status of women. Fertility differences are observed to be related to socioeconomic status amongst women in a country 11,14,15 . Understanding fertility behaviour is important for policy formulation and programme implementation. ...
Accelerating downwards the fertility rate remains a priority issue for developmental planning of any country. Fertility transition for most African countries has been slow since attaining their independence. In a period of almost four decades Eswatini experienced high fertility above replacement level fertility of 2.1; total fertility rate has declined from 6 to 4 children per woman from 1968 to 2007. This paper examines the impact of each proximate factor (contraception, postpartum infecundability, abortion and sexual activity) on fertility. Using the cross-sectional data from the 2006-7 Eswatini Demographic and Health Survey (DHS), the revised Bongaarts proximate determinants model of fertility was applied at national level and the analysis was extended to observe educational variation among women aged 15-49. The analysis showed that contraception had the greatest impact of fertility reduction, then sexual activity, postpartum infecundability and induced abortion. Women‘s educational level had huge negative influence on fertility and positive implication on reproductive choice of using contraception, delaying sexual activity/marriage and childbearing. The results guide on selection of potential social variables amenable to policy aimed at improving women‘s reproductive behaviour in Eswatini through better educational attainment.
... Education, an important and amenable policy variable in SSA, has rather a complex and ultimate negative relationship with fertility modifiable through a set of proximate determinants of fertility 13 . The benefits of education on fertility reduction are well articulated including exposing women to ideas of, and means to birth control or family planning 12,14,15 . However, accelerating fertility transition for SSA countries remains a challenge due to a significant proportion of illiterate women 14,16 . ...
... The Bongaarts framework was applied to quantify the impact on fertility of each proximate determinant (contraception, postpartum infecundability, sexual activity, and abortion) at national and subpopulation variation by level of education-a proxy for status of women. Fertility differences are observed to be related to socioeconomic status amongst women in a country 11,14,15 . Understanding fertility behaviour is important for policy formulation and programme implementation. ...
Accelerating downwards the fertility rate remains a priority issue for developmental planning of any country. Fertility transition for most African countries has been slow since attaining their independence. In a period of almost four decades Eswatini experienced high fertility above replacement level fertility of 2.1; total fertility rate has declined from 6 to 4 children per woman from 1968 to 2007. This paper examines the impact of each proximate factor (contraception, postpartum infecundability, abortion and sexual activity) on fertility. Using the cross-sectional data from the 2006-7 Eswatini Demographic and Health Survey (DHS), the revised Bongaarts proximate determinants model of fertility was applied at national level and the analysis was extended to observe educational variation among women aged 15-49. The analysis showed that contraception had the greatest impact of fertility reduction, then sexual activity, postpartum infecundability and induced abortion. Women's educational level had huge negative influence on fertility and positive implication on reproductive choice of using contraception, delaying sexual activity/marriage and childbearing. The results guide on selection of potential social variables amenable to policy aimed at improving women's reproductive behaviour in Eswatini through better educational attainment.
... The larger effects of coefficients compared to endowments indicate that the temporal changes in the reproductive behaviours of the different segments of the married women were important in the marital fertility transitions of the three countries. The greater importance of reproductive behaviour changes compared to compositional characteristics found in the current study arguably explains the parallel fertility trends for the different education categories previously reported by Garenne (2012). The longitudinal analysis of fertility rates by education status (no education, primary, secondary and tertiary) by Garenne (2012) showed that the differences in the fertility rates of women were explained by a time lag in the onset of transition. ...
... The greater importance of reproductive behaviour changes compared to compositional characteristics found in the current study arguably explains the parallel fertility trends for the different education categories previously reported by Garenne (2012). The longitudinal analysis of fertility rates by education status (no education, primary, secondary and tertiary) by Garenne (2012) showed that the differences in the fertility rates of women were explained by a time lag in the onset of transition. This means that family planning services which influence reproductive decisions and outcomes have been crucial to the marital fertility transitions of SSA countries. ...
Abstract Many studies of fertility have reported a wide range of factors to be important determinants. These determinants include proximate factors and male and female background variables. However, most of the research has been based on an analysis of cross-sectional fertility elasticities. Therefore, there are limited attempts to examine the temporal behaviour of especially marital fertility in association to the proximate and background determinants. To fill this gap, this study analysed Demographic and Health Survey data for Kenya, Rwanda and Zimbabwe using the Oaxaca-Blinder decomposition technique to determine the magnitude of marital fertility changes in association with selected socioeconomic factors. The results showed evidence of significant marital fertility transitions characterised by stalling for all three countries. Marital fertility rates were more responsive changes in reproductive behaviours than compositional characteristics. Male variables like community level of education were positively associated with stalling especially in Kenya and Zimbabwe. Despite the small contributions overall, analysing male and female variables improves the understanding of the sources of marital fertility changes in patriarchal societies.
... Women may also tend to report distant births as closer to the moment of the interview (Potter Effect). Finally some women just omit recent births, especially if children are deceased, to avoid additional or sensitive questions ( Kravdal, 2002;Fuchs, 2011;Garenne, 2012;Schoumaker, 2014;Towriss and Timaeus, 2017). ...
... La dernière interaction significative est celle de l'éducation avec le stade de la transition démographique. De nombreux auteurs affirment que la relation entre l'éducation et la fécondité est sans équivoque pendant la transition, mais c'es tpas le même avant et après le processus (Castro Martin, 1995 ;Clealand, 2002 ;Garenne, 2012). Clealand (2002) affirme en particulier que, dans une période pré-ou post-transition, le lien entre l'éducation et la fécondité est faible et diversifié, tandis que, pendant la transition, les écarts de fécondité par la scolarité atténuent jusqu'à la convergence. ...
This work is composed by three chapters, two of them deal with education and public education policies related to crime, one focuses on the relationship between education and birth spacing and fertility. In the first chapter I study the mechanisms at play between education and crime when the government introduces a policy to increase the access to education and whether choosing the right policy design we are able to reduce crime despite the raise in the aggregate wealth generated by human capital growth. In the second chapter I analyse the dynamic relation between education access, education quality and crime deterrence technology, to characterize the conditions under which crime drops and the implied role of education. The third chapter is an empirical study of the relationship between education and fertility in Sub-Saharan Africa, between economics and demography. Even if the topic and the methods of the first two papers differ a lot from the third one, they are all related by the interest to understand better the role of education in economic growth. Both crime and violence and high fertility rates and population growth, for diverse reasons and through peculiar dynamics, undermine economic investment and growth potential. The goal of this thesis is therefore to give a contribution to understand these reasons and these dynamics, with special attention to developing countries where free access to education is a recent achievement and where there is still work to do to improve the quality of the education system and teaching.
... It could be that as modernization became entrenched through increases in education and urbanization, the traditional restraints 8 on high fertility declined and were not compensated adequately by use of modern contraceptive methods. Another possible factor is probably the stagnation of provision of education services associated with the economic recession of 1976 to 1997, during which income per capita declined by some 20%, and probably reduced investments in the education sector (Garenne, 2012). ...
... In contrast, longitudinal studies show different results at aggregate level, when the transi- tion from natural fertility to the adoption of modern contraception is studied in response to changes in income, wealth and education over time. For example, an analysis of African countries between 1977 and 1999 shows that changes in per capita income and in level of education do not explain changes in fertility levels: most of the variations can be explained by changes in contraceptive use, age at first marriage, and urbanization, which follow other dynamics [21,26]. ...
This chapter reviews the undulated history of biodiversity in Israel -- from the species loss of mega fauna during the British Mandate -- to the conservation success of Israel's first sixty years -- to the recent retreat in trends and threats to the country's extraordinary biodiversity
Age at menarche was investigated using data collected from demographic surveys (WFS, DHS) conducted in Nigeria between 1982 and 2018, all of which were based on large representative samples of the female population. Linear-logistic regressions were used to estimate mean age at menarche, its trends and its risk factors. Mean age at menarche had underwent a marked secular decline from 15.02 years for girls born in 1933 to 13.78 years for girls born in 2003. In multivariate analysis, height (stature), body mass index (BMI), level of education and household wealth had independent effects on age at menarche, whereas urban residence had no effect. Socioeconomic gradients were large: +9 years of schooling was associated with a -0.52 year decrease in age at menarche, and +2 standard deviations in household wealth with a -0.33 year decrease. The impact of anthropometry was even greater: +2 standard deviations in height was associated with a -0.99 year decrease in age at menarche, and +2 standard deviations in BMI with a -1.42 year decrease. Northern provinces had a higher mean age at menarche than southern provinces. Compared with independent sources, long-term trends in age at menarche, as well as their fluctuations, appeared to be correlated with trends and fluctuations in income per capita and in under-five mortality, but not with divergent trends in adult height.
Population projection methods.
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The study of the US National Academy of Sciences (NAS) entitled Population Dynamics of Sub-Saharan Africa shows convincingly that demographic transition is underway in Botswana Kenya and Zimbabwe and that it may also have begun in Nigeria Senegal Lesotho Namibia Swaziland and South Africa. Preliminary survey results suggest levels of use of modern contraceptive methods among married women around 26% in Namibia 15% in Ghana and 17% in urban Burkina Faso. In 1989-93 the total fertility rate dropped from 6.7 to 5.4 in Kenya and the prevalence of modern contraception rose from 18% to 28%. The theoretical framework which appears to have molded the NAS panels expectations for the future is handicapped by its lack of historical perspective. Both perspectives assume that economic development is a precondition for fertility decline: either by increasing demand for small family size or by weakening the resilient sociocultural barriers to use of modern contraceptives. The new evidence strongly suggests that economic hardship is contributing to the reduction of ideal family size in the region. The Kenyan onset of fertility decline and significant increases in contraceptive use coincided with the economic reversal of the early 1980s. African populations developed an efficient system for ensuring high fertility and maximizing child survival through spacing of births. Components of the system were very early marriage for women polygyny ensuring universal marriage for women and quick remarriage of widows. Colonization increased the pressure for high fertility by introducing cash crops in African agriculture and imposing forced labor upon rural populations. This increased the need for labor placing an additional upward pressure on fertility. Understanding the African social supports for high fertility as a rational response to historical circumstances and not a peculiar expression of African cultures and social organization is a necessary condition both for advancing demographic transition theory and for designing culturally sound family planning programs.
Reliable data on HIV prevalence are essential for assessing the scope of and effectively managing the
response to the epidemic. Antenatal clinic-based surveillance is commonly used to monitor trends in HIV
in developing countries that have generalized epidemics. Recently, HIV seroprevalence data have been
also collected in national population-based surveys, such as the Demographic and Health Surveys (DHS)
and AIDS Indicators Surveys (AIS). Such surveys enable direct estimation of population HIV prevalence.
A major challenge for population-based surveys is bias resulting from non-response, both from refusal
and absence. In this study, we evaluate national HIV prevalence estimates from DHS and AIS surveys for
bias resulting from non-response in the surveys.
Data are from 17 recent national DHS and AIS surveys with HIV testing – Burkina Faso, Cambodia,
Cameroon, Cote d’Ivoire, the Dominican Republic, Ethiopia, Ghana, India, Kenya, Lesotho, Malawi,
Mali, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe – conducted during 2001 and 2006. Blood
samples were collected and tested for HIV using standard laboratory and quality-control procedures. In
the first three surveys, in Mali, Zambia, and the Dominican Republic, HIV status could not be linked to
the characteristics and behaviors of the survey respondents. For each of the other 14 countries with HIV
serostatus data linked to individual characteristics and behaviors, we predict HIV prevalence among nonresponding
adults on the basis of multivariate statistical models of HIV for those who were interviewed
and tested, using a common set of predictor variables. Predictions are made separately for two groups of
non-respondents: not interviewed/not tested and interviewed/not tested. Adjusted HIV prevalence is
calculated as a weighted average of observed prevalence in the interviewed/tested group and predicted
prevalence in the two non-tested groups. Predictions are made separately for adult males and females.
In the 14 countries with linked data, the HIV testing rate varied from a low of 63 percent among men in
Malawi and Zimbabwe to a high of 97 percent among women in Rwanda. Non-response rate was higher
among urban, more educated, and wealthier men and women but had no clear association with various
risk and protective behavioral factors. Non-tested men had significantly higher predicted HIV prevalence
than those tested in 7 of the 14 countries, and non-tested women had significantly higher predicted
prevalence than those tested in 5 of the 14 countries. Although non-tested men and women tend to have
higher predicted HIV prevalence than those tested, the overall effect of non-response bias on observed
prevalence estimates was small and not significant in all countries. In the 14 countries, HIV prevalence
estimates adjusted for non-response bias were on average only 3 percent and 2 percent higher than the
observed, non-adjusted estimates for men and women, respectively.
The study finds that non-response for HIV testing tends to have small, non-significant effects on national
HIV seroprevalence estimates obtained from national household surveys. National population-based
surveys are an important source of reliable data on HIV prevalence that can enhance surveillance-based
estimates in generalized epidemics.
Reliable data on HIV prevalence are essential for assessing the scope of and effectively managing the
response to the epidemic. Antenatal clinic-based surveillance is commonly used to monitor trends in HIV
in developing countries that have generalized epidemics. Recently, HIV seroprevalence data have been
also collected in national population-based surveys, such as the Demographic and Health Surveys (DHS)
and AIDS Indicators Surveys (AIS). Such surveys enable direct estimation of population HIV prevalence.
A major challenge for population-based surveys is bias resulting from non-response, both from refusal
and absence. In this study, we evaluate national HIV prevalence estimates from DHS and AIS surveys for
bias resulting from non-response in the surveys.
Data are from 17 recent national DHS and AIS surveys with HIV testing – Burkina Faso, Cambodia,
Cameroon, Cote d’Ivoire, the Dominican Republic, Ethiopia, Ghana, India, Kenya, Lesotho, Malawi,
Mali, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe – conducted during 2001 and 2006. Blood
samples were collected and tested for HIV using standard laboratory and quality-control procedures. In
the first three surveys, in Mali, Zambia, and the Dominican Republic, HIV status could not be linked to
the characteristics and behaviors of the survey respondents. For each of the other 14 countries with HIV
serostatus data linked to individual characteristics and behaviors, we predict HIV prevalence among nonresponding
adults on the basis of multivariate statistical models of HIV for those who were interviewed
and tested, using a common set of predictor variables. Predictions are made separately for two groups of
non-respondents: not interviewed/not tested and interviewed/not tested. Adjusted HIV prevalence is
calculated as a weighted average of observed prevalence in the interviewed/tested group and predicted
prevalence in the two non-tested groups. Predictions are made separately for adult males and females.
In the 14 countries with linked data, the HIV testing rate varied from a low of 63 percent among men in
Malawi and Zimbabwe to a high of 97 percent among women in Rwanda. Non-response rate was higher
among urban, more educated, and wealthier men and women but had no clear association with various
risk and protective behavioral factors. Non-tested men had significantly higher predicted HIV prevalence
than those tested in 7 of the 14 countries, and non-tested women had significantly higher predicted
prevalence than those tested in 5 of the 14 countries. Although non-tested men and women tend to have
higher predicted HIV prevalence than those tested, the overall effect of non-response bias on observed
prevalence estimates was small and not significant in all countries. In the 14 countries, HIV prevalence
estimates adjusted for non-response bias were on average only 3 percent and 2 percent higher than the
observed, non-adjusted estimates for men and women, respectively.
The study finds that non-response for HIV testing tends to have small, non-significant effects on national
HIV seroprevalence estimates obtained from national household surveys. National population-based
surveys are an important source of reliable data on HIV prevalence that can enhance surveillance-based
estimates in generalized epidemics.
This study examines fertility decline in North-Central Namibia in the period 1960-2000. A Scandinavian-type parish-register system, established in the beginning of 20th Century and still in use, provided register-based data for fertility analysis. Fertility decline began in 1980, was rapid in the 1980s, levelled off in the early 1990s, started again in 1994 and continued until the year 2000. Fertility declined in every age group, except among the 15-19 year olds, whose fertility increased. Cohort fertility started to decline among the 1940-44 birth cohort. During the 1980s, fertility decline was associated with increasing age at first marriage and declining marital fertility, connected to e.g. the War of Independence. During the 1990s, an increase in both the use of contraceptives and HIV-prevalence contributed to the fertility decline.