The Effect of Women’s Incarceration on the Fertility Rate

Prospects for Fertility Decline in Africa

This research has two main goals: (i) to examine fertility desires (number of children) in sub-Saharan Africa: levels as compared to other major regions and recent trends; and (ii) to assess the extent to which fertility decline in sub-Saharan Africa is contingent on decline in fertility desires (singly and in combination with other reproductive changes).

As a continent with 54 independent states Africa’s diversity is often highlighted but frequently forgotten when fertility is discussed. Fifty and more years ago to consider that all African countries and societies had a single fertility pattern (large numbers of children) and single trend (unchanging over time) was a valid characterization. Since the 1960s however that uniformity has disappeared replaced by substantial inter- and intra-country differences in fertility patterns and trends that render previous perceptions of continent-wide homogeneity obsolete. In this chapter we consider two African countries—Ghana and Kenya—whose fertility patterns and trends and their determinants have been well documented (Bongaarts 2008; Garenne 2008; Machiyama 2010; Shapiro and Gebreselassie 2008; Sneeringer 2009). Both countries have benefited from regularWorld Fertility Surveys (WFS) and Demographic and Health Surveys (DHS) that record trends in fertility family planning (FP) and other relevant indicators. The recently introduced Performance Monitoring and Accountability 2020 (PMA2020) surveys monitor progress since 2012 for the FP2020 initiative and occasional Situation Analysis and Service Provision Assessment surveys have also detailed the readiness of the health system in both countries to make quality FP services available. Ghana and Kenya share some common history: both have relatively strong health system legacies from the period of British colonialization; both were among the earliest countries to achieve independence; they were the first two African countries that developed policies to address population growth in the 1960s; and both have received substantial and sustained resources over several decades from many external donors and technical assistance organizations explicitly intended to increase the availability and quality of family planning services. However they are composed of cultures that are both diverse within each country and markedly different in many ways between the two countries. The two countries demonstrate remarkably different pathways in fertility and family planning patterns and trends from the 1970s to the present. We highlight some of the key differences and similarities explain why they have occurred and identify insights that could inform a wider understanding of fertility transitions and the role of family planning in other African countries. (excerpt)

1. IntroductionThe purpose of this paper is to analyze the changes in the level and timing of fertility in Uruguay between 1996 and 2011, using period fertility measures by birth order. The analysis of the evolution of fertility trends by birth order can contribute to the understanding of the three main processes in the transition towards a low-fertility setting: the reduction of higher-order births through parity-specific fertility control, the increase in the proportion of women who remain childless throughout their reproductive years, and the postponement of childbearing. Each one of these processes is closely related to the others and together they typically cause the decline of the conventional period total fertility rate (TFR).With some exceptions (Batyra 2015; Miranda-Ribeiro, Rios-Neto, and Ortega 2008; Rios-Neto and Miranda-Ribeiro 2015), the analysis of the recent decline in the TFR in Latin American countries has relied exclusively on data on births by age of the mother. While several European and North American countries have long series of data on birth counts by both age of the mother and birth order, they are rare to find in Latin America countries with the exceptions of Chile, Costa Rica, and - recently - Uruguay (Lima et al. 2015; Rosero-Bixby, Castro-Martin, and Martin Garcia 2009).For this study we constructed the 1996-2011 series of births by age of the mother and birth order by using individual birth registers from the Live Birth Certificate and the Perinatal Information System of Uruguay, and estimated age- and birth-orderspecific fertility rates and summary measures of timing and quantum of fertility. We analyzed this set of indicators to demonstrate the extent of family limitation, childlessness, and postponement in the evolution of fertility for synthetic cohorts during the recent decline of the period TFR in Uruguay.Studying fertility by birth order may allow a more rigorous analysis of the demographic components of recent changes in fertility and the construction of new measures will allow the comparison of fertility decline in Uruguay with those of other, mostly European countries.As seen in several developed countries, a shift in the timing of childbearing can inflate or deflate the conventional period TFR (Bongaarts and Sobotka 2012). In a recent work, Pardo and Cabella (2014) estimated tempo-adjusted (TFR*) and tempoand parity-adjusted (TFRp*) total fertility rates for Uruguay for the years 1996-2011. They found that TFR decreased almost entirely by quantum decline, as the tempo effect only accounted for TFR change through a peak in the 3-4 worst years of the economic crisis that hit the country between 1998 and 2004 and a mild increase in the last years. In this paper we also apply tempo-adjusted period fertility indicators to compare their trends with the developments in the conventional period TFR, examining to what extent the two differed over time and how sizeable the recent impact of the tempo effect on Uruguayan TFR is. Furthermore, the use of the tempo- and parity-adjusted measures of quantum fertility by birth order would allow breaking down changes in tempo-adjusted fertility into their birth order components, possibly providing more accurate evidence on the role of parity-specific fertility declines in the observed changes in period fertility.The structure of the article is as follows. First, it provides some background on fertility decline in Latin America and the Caribbean, and specifically Uruguay. Then, the context of extensive socioeconomic and family change within which it has taken place is discussed. Next, the data and methods used are described and fertility level and timing by birth order are reported, including conditional age-specific fertility rates by birth order and summary measures on both dimensions. Finally, mean age at first birth and its standard deviation in Uruguay is compared with those of other selected countries and some final remarks are made. …

Fertility decline in most countries of sub-Saharan Africa has thus far started later and proceeded more slowly than in countries in Asia and Latin America and the Caribbean undergoing the transition in fertility since the 1950s from high levels to near-replacement or even below-replacement levels (Bongaarts and Casterline 2013). Yet there is considerable variation among countries in sub-Saharan Africa: in the duration and magnitude of fertility decline, whether stalls in fertility decline have occurred, shifts in the timing of births, and even the economic and population subgroups that have led declines in family size (Caldwell, Orubuloye, and Caldwell 1992; Bongaarts and Casterline 2013; Cleland, Onuoha, and Timæus 1994; Cohen 1998; Ezeh, Mberu, and Emina 2009; Garenne 2008; Kirk and Pillet 1998; Rossier, Corker, and Schoumaker 2015; Timæus and Moultrie 2008). With current fertility estimated at 5.1 births per woman in the region and 19 countries in sub-Saharan Africa at or above that level and another 21 countries with at least four births per woman on average (United Nations 2015a), the pathways that future fertility takes will significantly determine population growth and age structure shifts not only in the region, but increasingly for the world. Sub-Saharan Africa is projected to grow from 840 million people in 2010 to nearly 1.4 billion in 2030 (United Nations 2015a). Above-replacement fertility is projected to account for 61 percent of this population increase from 2010 to 2030 compared to 4 percent from mortality reduction, 37 percent from a young age structure in 2010 (population momentum), and a small negative contribution from migration (United Nations 2015b). These projections draw on the United Nations medium variant and do not take into account the uncertainty around current and future fertility levels, uncertainty that only increases the farther the projection period extends. For high-fertility countries in sub-Saharan Africa, the wide uncertainty around where fertility is headed can result in substantial differences in population projections (Ezeh, Mberu, and Emina 2009; Fuchs and Goujon 2014; Gerland et al. 2014). Beyond population numbers alone, the uncertainty about fertility decline also bears on policy-relevant questions such as the degree to which the region may realize a demographic dividend (e.g., how fast the shift will occur toward a higher ratio of working-age population to non-working-age population and the consequent effects on economic growth) (Bloom et al. 2013) or the extent to which greenhouse gas emissions might be reduced by slowing population growth (O'Neill et al. 2010). Earlier reviews of the fertility transitions in sub-Saharan Africa through the 1980s and 1990s showed fertility declines underway in most countries and particularly rapid declines in several countries in Eastern Africa (Kenya, Rwanda, and Zimbabwe) and Southern Africa (Botswana and South Africa) (Cleland, Onuoha, and Timæus 1994; Cohen 1998; Kirk and Pillet 1998). Subsequent survey data suggested an apparent slowdown in the pace of fertility decline in more than ten sub-Saharan African countries (Bongaarts 2008). Further analyses of the data indicated far fewer stalls in fertility decline had in fact occurred in the region, with evidence strongly supportive of stalls in Kenya and Rwanda, and the stalls that do occur have been of relatively short duration (Garenne 2011; Machiyama 2010; Schoumaker 2009, 2014). Most countries in sub-Saharan Africa still lack complete and accurate vital registration data on births, so these and other analyses of fertility trends will continue to rely heavily on survey data and require reconciling estimates from different sources (Alkema et al. 2012; United Nations 2015c). Our aim in this chapter is to provide an updated and concise description of the diversity of fertility decline patterns among countries1 in sub-Saharan Africa, drawing on the latest series of fertility estimates that take into account many different data sources and that are harmonized with other demographic components (United Nations 2015d). We focus on the level of fertility prior to the start of fertility decline, the time period of the fertility transition, and the estimated pace of decline. We also explore the implications of different fertility decline patterns for future fertility and population projections in the region. We draw on the distinct patterns of fertility decline among countries worldwide that are advanced in (or have completed) their first fertility transition to construct probabilistic fertility and population projections for sub-Saharan African countries. The illustrative comparisons of projections highlight the demographic impact if future fertility decline in sub-Saharan countries were to accelerate and follow the rapid pace of decline already experienced by a diverse group of countries. The UN Population Division publishes estimates and projections of period total fertility rates in World Population Prospects (WPP) every two years. The estimates of total fertility presented in this chapter are from the 2015 Revision and are for five-year time periods for countries or areas with 90,000 persons or more in 2015 (United Nations 2015a). The most recent data underlying the total fertility estimates from the 2015 Revision for 50 sub-Saharan African countries2 (United Nations 2015c) are from the period 2013–2014 for 14 countries, 2010–2012 for 30 countries, and 2005–2009 for six countries. A common challenge in estimating total fertility over time, especially for countries without accurate or complete vital registration data,3 is that estimates vary across data sources and by the methodology used to derive those estimates. Schoumaker (2014) showed that the underlying data from standardized, high-quality surveys such as the Demographic and Health Surveys vary considerably within and across countries, yielding total fertility estimates from recent fertility data of good quality (e.g., Gabon, Lesotho, Namibia, and Zimbabwe) and of poor quality (e.g., Benin, Burkina Faso, Cameroon, Chad, Ethiopia, Guinea, Madagascar, Mali, Mozambique, Niger, Nigeria, and Uganda). Total fertility estimates based on births in the last three years tend to be under-estimated by 10 percent or more in most of the surveys with poor quality fertility data from retrospective birth histories. Figure 1 illustrates the variation in total fertility estimates based on survey data and estimation methods (direct methods (D) and cohort-completed (C) fertility) for Nigeria for the period 1985 to 2015. The thick lines show the total fertility estimates from the 2010, 2012, and 2015 Revisions of WPP. Given new data from the 2008 DHS and other surveys, total fertility in the 2012 Revision was re-estimated at a higher level than the 2010 Revision beginning in the mid-1980s, resulting in almost half a birth per woman difference in the 2005–2010 period. The 2015 Revision used new data from the 2013 Demographic and Health Survey. The 2013 survey estimates highlight a recurring pattern in which fertility estimates based on a recent reference period are consistently lower than fertility estimates from reconstructed birth histories for the same time point. Looking only at fertility estimates from a three-year reference period, the 2013 DHS shows a decline in total fertility to 5.5 births per woman from a stalling pattern of 5.7 births per woman in the 2003 and 2008 DHS. Yet the absolute differences are large between these three-year reference period estimates and those for the same time point from the reconstructed birth histories: about half a birth difference in the mid-2000s (comparing the 2008 and 2013 survey estimates) and about one birth difference in the early 2000s (comparing the 2003 survey estimate to those from the 2008 and 2013 surveys). Estimates of the total fertility rate for Nigeria 1985–2015 based on various data sources and estimation methods, and WPP estimates from the 2010, 2012, and 2015 Revisions NOTES: DHS = Demographic and Health Survey; MICS = Multiple Indicator Cluster Survey; MIS = Malaria Indicator Survey. (C) refers to cohort completed fertility (i.e., average number of children ever born) for women aged 40–44 and 45–49 at the date of the survey and backdated using their mean age of childbearing. (D) refers to direct fertility estimates based on maternity histories or recent births in the 12 or 24 months preceding the survey. SOURCES: Federal Office of Statistics of Nigeria (1992); National Bureau of Statistics (2008, 2012); National Population Commission (2000, 2002, 2004, 2009, 2012, 2014). WPP fertility estimates consider as many types and sources of empirical estimates as possible, including retrospective birth histories and direct and indirect fertility estimates (Gerland 2014). The 2015 Revision updated all total fertility estimates taking into account new data and the inconsistencies among estimates. Moreover, total fertility estimates are derived to ensure as much internal consistency as possible with all other demographic components and intercensal cohorts enumerated in successive censuses (United Nations 2015d). The advantages of this approach are that the estimates are internally consistent within a country over time and with respect to other related demographic information. A disadvantage is that the estimates can depart from a country's official estimates of fertility. Figure 2 shows the estimated trends in period total fertility for sub-regions of sub-Saharan Africa from 1950 to 2015. It was high (above six births per woman) in all sub-regions in 1950–1955. Fertility remained high in Eastern and Western Africa until the 1980s, after which it began a slow decline to 4.9 births per woman in Eastern Africa and 5.5 in Western Africa in 2010–2015. Fertility in Middle Africa began to decline a decade later and more slowly, reaching 5.8 births per woman in 2010–2015. Southern Africa departed from the overall trends with a decline beginning in the 1950s and dropping below three births per woman in the 2000s. The 2010–2015 estimate of 2.5 births per woman in Southern Africa is about half the total fertility level in Eastern, Middle, and Western Africa. Sub-regional trends in total fertility, sub-Saharan Africa, 1950–2015 SOURCE: United Nations 2015a The sub-regional fertility levels mask diverse levels among countries. Figure 3 shows country-specific total fertility levels in 2010–2015. Among the 16 countries in Western Africa, total fertility ranged from 2.4 in Cabo Verde to 7.6 in Niger. One additional country had a fertility level of more than six births per woman (Mali), six countries had fertility between five and six births per woman (Burkina Faso, Côte d'Ivoire, Gambia, Guinea, Nigeria, and Senegal), and seven had fertility between four and five births per woman. Total fertility levels in countries in Africa, 2010–2015 SOURCE: United Nations 2015a. Total fertility levels ranged even more widely among the 20 countries in Eastern Africa, from 1.5 in Mauritius to 6.6 in Somalia. One additional country in Eastern Africa still had a fertility level above six in 2010–2015 (Burundi) and six countries had fertility between five and six births per woman (Malawi, Mozambique, South Sudan, Uganda, Tanzania, and Zambia). Eight countries had fertility levels between four and five births per woman. The lowest levels of fertility were in Djibouti (3.3 births per woman) and in the small island countries of Mauritius, Réunion, and Seychelles (less than three births per woman). The nine countries of Middle Africa all had fertility levels at or above four children per woman. In Angola, Chad, and DR Congo, fertility levels were six or more births per woman, and in the remaining six countries fertility levels were between four and five births per woman. While fertility in Southern Africa is largely dominated by South Africa's pattern, the range in fertility among the five countries in the sub-region is narrow, from 2.4 births per woman in South Africa to 3.6 in Namibia. Both Botswana and South Africa now have fertility levels below three births per woman. The start of the fertility transition also varies widely across sub-Saharan Africa. We analyze when and at what level of fertility a country experienced a maximum total fertility level before the onset of fertility decline. This maximum is defined as the most recent five-year time period where total fertility is within half a child of the overall maximum fertility in the country over the 1950–2015 estimation period, thus excluding random fluctuations in pre-transition fertility (Alkema et al. 2011). Figure 4 shows the diversity across countries and within sub-regions in the level and time period of the maximum fertility level before the onset of fertility decline, as assessed in the 2015 Revision. By the late 1970s, 29 sub-Saharan countries were on the verge of a fertility decline, increasing to 40 countries by the early 1980s. While the maximum fertility before the onset of fertility decline was reached in all countries in Southern Africa by the late 1970s, the range of experiences was much wider among countries in Eastern Africa (from the early 1950s in Réunion to the late 1990s in Somalia), Middle Africa (from the late 1960s in Angola to the late 1990s in Chad and DR Congo), and Western Africa (from the early 1960s in Cabo Verde to the late 1990s in Niger). Maximum total fertility in the time period before the onset of fertility transition, sub-Saharan African countries by sub-region SOURCE: United Nations 2015a. The maximum fertility before the onset of fertility transition ranged from less than six births per woman in five countries (Central African Republic, Equatorial Guinea, Gabon, Lesotho, and Seychelles) to more than eight births per woman in two countries (Kenya and Rwanda). There was a positive but weak relationship between the maximum fertility level and the timing of when fertility transition commenced (R2 = .04). The transition from the maximum fertility to the current estimated fertility level in 2010–2015 has been slow for most countries in sub-Saharan Africa. We examine both the maximum decline in fertility in a five-year period and the duration of time between the maximum fertility level and when a country achieved a 10 percent decline in total fertility. A 10 percent decline in total fertility is one of several empirical rules that researchers have used to identify when fertility has begun to decline in a sustained manner from a pre-transitional maximum (Bongaarts and Casterline 2013; Coale and Treadway 1986; United Nations 2014), including differences in the time period selected to identify the onset of fertility decline (Casterline 2001) and the magnitude of decline (e.g., 5 percent decline with further conditions for subsequent changes applied, see Bryant 2007). While the different rules all strive to distinguish sustained decline from random fluctuations in fertility levels, each rule will have different repercussions for interpretations about the timing of onset of fertility decline, duration and pace of decline, and correlations with levels of development and other indicators. In seven countries the first steps of fertility decline took place over a long time period. In Angola, Gambia, and Uganda, a 10 percent decline from the maximum fertility level took 40 years; and in Lesotho, Mozambique, Niger, and Tanzania, it took 30 to 35 years (Appendix Table 1).4 In other countries the fertility decline never gained speed. Overall declines from the country-specific maximum fertility level to the level in 2010–2015 were very slow in nine countries (Angola, Congo, Gambia, Mali, Mozambique, Niger, Nigeria, Uganda, and Tanzania), where average fertility declines were 0.2 children per woman or less, a pace at which it would take at least 25 years to realize a decline of one birth per woman (Appendix Table 1). During the fertility transition, some countries experience an acceleration of fertility decline that, based on historical experience of past transitions in Latin America and the Caribbean and Asia, might reach a decline of more than one birth per woman in a five-year period (United Nations 2015a). In most countries in sub-Saharan Africa, however, the maximum fertility decline is much smaller. The largest declines of more than one birth per five-year period were registered in the early-transition countries in Eastern Africa before 2000 (Djibouti, Mauritius, Mayotte, Réunion, Rwanda, Seychelles, and Zimbabwe). For some countries the maximum fertility decline is projected to be reached in the future: one country in Eastern Africa (Mozambique), four in Middle Africa (Angola, DR Congo, Equatorial Guinea, and Sao Tome and Principe), and five in Western Africa (Gambia, Guinea, Mali, Niger, and Nigeria). Figure 5 shows that countries reaching the maximum fertility decline later tend to reach it at lower levels (the correlation across the 50 countries is R2 = .45). Maximum five-year decline in total fertility since the onset of fertility transition, actual and projected, sub-Saharan African countries by sub-region SOURCE: United Nations 2015a. The pace of fertility decline is illustrated in Figure 6 for Ethiopia and Nigeria, the two most populous countries in sub-Saharan Africa. Estimated levels of total fertility from 1950–1955 to 2010–2015 are shown with a fitted model of the fertility transition. Both countries reached maximum fertility levels before the onset of fertility decline between the late 1970s and early 1980s, and both reached a 10 percent decline from this maximum in the early 2000s. While Ethiopia reached the peak pace of fertility decline in 2005–2010 (a decline of nearly one birth in the five-year period), Nigeria's fertility decline has been consistently slow, with a low peak pace of decline. The maximum fertility decline per five-year period in Nigeria is projected to be reached in the future (a decline of 0.3 births in 2020–2025). The difference in the pace results in current total fertility of 4.6 births per woman in Ethiopia and 5.7 in Nigeria. The 80 percent prediction intervals (dashed lines in Figure 6) around the probabilistic projections of total fertility indicate the magnitude of the different fertility changes that could reasonably happen. For example, by 2045–2050 there is a one in ten chance that total fertility in Nigeria could be as low as 2.6 or as high as 4.4 (Figure 6 and Appendix Table 2). Observed decline in total fertility from 1950–1955 to 2010–2015, predicted decline, and time period of maximum TFR and maximum decline, Ethiopia and Nigeria SOURCES: United Nations 2015a and computations by authors. Given the slow pace of fertility decline in most sub-Saharan African countries thus far and the uncertainty around the pace of future declines, what might be the impact on future fertility levels and the growth in total population if fertility declines in the region followed an accelerated pace that has already been experienced by countries that have completed or are nearing completion of the fertility transition? To answer this question, we generated probabilistic fertility scenarios for 2015–2100 using a modeling approach described in Alkema et al. (2011) and implemented in a publicly accessible software package BayesTFR (Ševčíková, Alkema, and Raftery 2011). The standard United Nations 2015 probabilistic results pool the experience of all countries having similar fertility levels and trends (the baseline scenario). An alternative probabilistic scenario was created using only the fertility transition experiences of 21 countries5 that shared a similar pattern of accelerated decline in fertility (the accelerated scenario): specifically, a slow pace at the start of the transition that sharply rises to a peak pace of decline before steadily tapering off after total fertility has reached about four births per woman. These 21 countries, which include—among countries with more than 10 million inhabitants in 2015—Bangladesh, China, El Salvador, Morocco, Peru, South Africa, Sri Lanka, Thailand, Turkey and Uzbekistan, represent disparate institutional, social, economic, and cultural contexts and yet experienced a similar pattern of a relatively abrupt acceleration of fertility decline. The objective of this exercise is to examine the impact on population size of fertility decline that is more rapid than currently projected, and to illustrate the implications of such a hypothetical scenario. While we do not theorize or analyze the factors that produced these rapid fertility declines, the fact that a similar pattern of fertility decline took place within such a diverse group of countries raises the possibility that a similar decline could occur within a region that also reflects quite distinctive and diverse contexts. Each fertility scenario is used to simulate 10,000 probabilistic population projections for 2015–2100 under identical conditions (i.e., using the same mortality and migration assumptions) that show the population growth trajectories if sub-Saharan African countries were to follow a specific fertility decline pattern.6 The implications of this specific fertility decline pattern for future fertility trends are shown in Figure 7 for Ethiopia and Nigeria. The accelerated fertility decline scenario leads to much more rapid declines for these countries. By 2045–2050, median projected total fertility in Ethiopia would decline from 2.3 (baseline) to 2.0 (accelerated decline) and in Nigeria from 3.6 (baseline) to 2.6 (accelerated decline) (see Appendix Table 2). Probabilistic fertility projections (median and 80 percent prediction intervals) for two scenarios: baseline and accelerated fertility decline, Ethiopia and Nigeria SOURCES: United Nations 2015a and computations by authors. The accelerated fertility scenario has greater implications for projected fertility in Nigeria, where the recent pace of fertility decline has been much slower than in Ethiopia. The projected medians of the accelerated scenario are similar to the lower bound of the baseline scenario for parts of the projections, suggesting that Ethiopia and Nigeria would need to experience a rapid fertility decline, similar to the rapid declines experienced by the group of 21 countries, in order to realize the lower-bound fertility level of the baseline scenario. The assumption that Ethiopia and Nigeria would follow the fast fertility decline experiences of these 21 countries leads to substantially lower population projections compared with the baseline scenario (Figure 8 and Appendix Table 3). If Ethiopia experienced an accelerated fertility decline, the projected population grows from 99 million in 2015 to 182 million by 2100 (80 percent prediction interval of 93 million to 324 million) compared with the higher median projection of 234 million under the baseline scenario. If Nigeria adopted the accelerated fertility decline pattern, the projected population grows from 182 million in 2015 to a median of 466 million by 2100 (80 percent prediction interval of 282 million to 777 million) rather than the faster projected growth to 737 million under the baseline scenario. Probabilistic population projections (median and 80 percent prediction intervals) for two scenarios: baseline and accelerated fertility decline, Ethiopia, Nigeria, and sub-Saharan Africa SOURCES: United Nations 2015a and computations by authors. The counterfactual of sub-Saharan African countries following an accelerated fertility decline, as experienced by the group of 21 countries, results in a projected increase in total population in the region from 962 million in 2015 to 3.2 billion by the end of the century (with 80 percent probability of being between 2.8 and 3.7 billion). The total population projection for the baseline UN 2015 scenario would lead to a median projection of about 4 billion, or a difference of 770 million people with an 80 percent prediction interval mostly above the upper bound of the accelerated fertility decline scenario. We presented here an updated description of fertility decline in sub-Saharan Africa and have explored the implications for fertility and population projections if sub-Saharan African countries follow a pattern of accelerated fertility decline. Because estimates of fertility in the region rely almost entirely on survey and census data, there is still uncertainty about fertility change over time. Our analyses of trends over time were based on a new historical time series of total fertility estimates for the period 1950 to 2015 from the 2015 Revision of World Population Prospects. Fertility has remained persistently high in Eastern, Middle, and Western Africa (4.9 births per woman or higher as of 2010–2015) and declined rapidly in Southern Africa, which currently has about half the total fertility level of other sub-regions births per woman). Southern Africa is dominated by South Africa, TFR of 2.4 is the lowest in the Yet there is wide variation in total fertility across countries, even within from below fertility births per woman) in Mauritius to 7.6 births per woman in Niger. The fertility level before the onset of decline was high in all countries in sub-Saharan Africa, from less than six births per woman in five countries to more than eight births per woman in two countries. By the late 1970s, 29 countries were on the verge of fertility decline, increasing to 40 countries by the early 1980s. The transition from these maximum levels of fertility prior to the onset of fertility decline to current levels has been slow for most countries, including seven in which it took 30 years or more to a 10 percent decline in total fertility. While countries in Asia and in Latin America and the Caribbean experienced an acceleration in fertility decline of more than one birth per five-year time period, this level of acceleration has been experienced thus far by only seven countries in Eastern Africa before the The pace of fertility decline in sub-Saharan Africa will a large in the magnitude of future growth in We showed through an illustrative scenario that an acceleration in the pace of fertility decline to that already experienced by 21 countries South Africa) would the growth in future population in the region from a projected billion by to billion and total population size from a projected 4 billion people by the end of the century to 3.2 While these 21 countries represent a wide range of institutional, social, economic, and cultural a similar fast decline from fertility levels of six or more children per woman in the past to less than two or three in recent years. this accelerated pace of fertility decline to countries at of transition illustrates the substantial impact on future population growth of other possible patterns of fertility decline in sub-Saharan Africa. of this are assumption that the fertility estimates from the World Population Prospects are and the fact that uncertainty around the estimates of period total fertility in recent is not into By scenarios on the distinct fertility decline patterns that have been experienced thus far by countries that have completed or are advanced in their fertility we new patterns of fertility decline in the Because most countries in Middle Africa and Western Africa are still in the early of fertility transition, an sub-Saharan African pattern of fertility decline may still and Timæus The is not for the or of by the authors. than be to the for the

This thesis studies two topics in the macro-labour literature: investment in human capital and fertility decisions. The thesis comprises of three chapters, the first studying the effects of private tutoring in Korea and its resemblance to a human capital rat race and the second and third investigating the rapid decline in fertility rates experienced in developing countries over the past few decades. With many countries having reached universal primary and secondary education, parental spending on education for supplementary and enrichment purposes has begun to resemble a rat race. In many Asian countries, it is normal for students to receive some or several forms of private tutoring alongside formal schooling. However, unlike the returns to schooling or the effects of school quality on student achievement which have been widely studied, the effects of private tutoring have received limited attention. In the first substantive chapter of this thesis, I exploit exogenous variation in spending on private tutoring caused by the imposition of a curfew on the operating hours of tutoring institutes in Korea, to estimate the impact of spending on tutoring on long-term educational and labour market outcomes. The first stage estimates highlight the severity of the rat race, with curfews imposed as late as 10pm still constraining tutoring expenditure. While I do not find any significant effects of tutoring expenditure on entering college, when I interact tutoring expenditure with parental education, I find a significant, positive effect of tutoring on attending any college for children of less educated parents, while the effect for children of more educated parents is not significantly different from zero. Given that the less educated parents spend much less on tutoring, these results indicate diminishing marginal effects of tutoring, while the lack of an effect in the specification linear in tutoring expenditure points to the average impact (local to those constrained by the curfew) being close to zero. I also find that tutoring expenditure has a positive effect on both completing a four year degree and on being employed. I place these empirical findings within an asymmetric information framework to explain how the use of test scores as a signal for ability leads to inefficiently high investments in tutoring, leading to a rat-race equilibrium. The second paper highlights the trends in fertility rates observed in developing countries, pointing out that cross-country differences in fertility rates have fallen very rapidly over the past four decades, with most countries converging to a rate just above two children per woman. In the second substantive chapter in the thesis, my co-author and I argue that the convergence in fertility rates has taken place despite the limited (or absent) absolute convergence in other economic variables and propose an alternative explanation for the decline in fertility rates: the population-control programmes started in the 1960s which aimed to increase information about and availability of contraceptive methods, and establish a new small-family norm using public campaigns. Using several different measures of family planning programme intensity across countries, we show a strong positive association between programme intensity and subsequent reductions in fertility, after controlling for other potential explanatory variables, such as GDP, schooling, urbanisation, and mortality rates. We conclude that concerted population control policies implemented in developing countries are likely to have played a central role in accelerating the global decline in fertility rates and can explain some patterns of that fertility decline that are not well accounted for by other socioeconomic factors. In the third main chapter of the thesis, we build on the findings presented in the previous chapter by studying a quantitative model of endogenous human capital and fertility choice, augmented to portray a role for social norms over the number of children. The model allows us to gauge the role of human capital accumulation on the decline in fertility and to simulate the implementation of population-control policies aimed at affecting social norms on family size. We also consider extensions of the model in which we allow a role for the decline in infant and child mortality and for improvements in contraceptive technologies (the second main component of the population-control programmes). Using data on several socio-economic variables as well as information on funding for family planning programmes to parametrise the model, we find that, as argued in the previous chapter, policies aimed at altering family-size norms provided a significant impulse to accelerate and strengthen the decline in fertility that would have otherwise gradually taken place as economies move to higher levels of human capital and lower levels of mortality.

Proximate determinants of Palestinian fertility: a decomposition analysis

Causes and Characteristics of Population Aging

ABSTRACTBackground and AimsFertility and child mortality are critical public health indicators in India, directly influencing health policy, planning, and intervention effectiveness. The relationship between declining fertility and decreasing child mortality has been widely debated. This study aims to estimate and compare fertility and child mortality rates at the district level using data from the National Family Health Survey (NFHS) rounds 4 and 5, with a focus on understanding regional trends and their implications for health interventions.MethodsThe study investigates four key demographic indicators: Total Fertility Rate (TFR), Neonatal Mortality Rate (NMR), Infant Mortality Rate (IMR), and Under‐Five Mortality Rate (U5MR). Fertility and child mortality rates were estimated using Bayesian methods, aligned with Demographic and Health Survey (DHS) standards. Fertility rates were computed in two stages: first, birth history data were transformed into table of birth, followed by the calculation of fertility rates through Poisson regression models at the district level.ResultsBetween 2015–16 and 2019–21, the number of districts with a TFR below 1.6 increased from 21 to 166, while number of districts with a TFR between 1.6 and 2.1 remained stable at 328. Additionally, the number of districts with an NMR under 10 per 1,000 live births grew from 79 to 140. The study found a strong association between the reduction in child mortality and the decline in fertility rates.ConclusionThis study suggests that addressing regional variations in fertility and child mortality rates could enhance the effectiveness of health interventions in India. Policymakers should prioritize expanding access to family planning and maternal‐child health services. The availability of district‐level data will support more targeted and effective health policies tailored to local needs.

ObjectivesThis study aimed to analyse temporal trends in fertility and birth rates, examine maternal characteristics and forecast future demographic changes in Georgia.DesignThis was a retrospective observational study using population-level data from the National Statistics Office of Georgia (Geostat) and the Georgian Birth Registry (GBR). Temporal trends were analysed using Prais-Winsten regression models and annual percentage changes (APCs) and future projections were generated using autoregressive integrated moving average (ARIMA) models.ParticipantsThe study included data on 543 662 live births retrieved from the Geostat for the period 2014–2024. Additionally, maternal characteristics were analysed for 366 684 births recorded in the GBR for the period 2017–2024. Selection criteria included all live births during the study period, with no specific exclusion criteria applied.Primary and secondary outcome measuresPrimary outcome measures were trends in the total fertility rate (TFR) and crude birth rate (CBR) over the study period (2014–2024) and their projections for 2025–2034. Secondary outcome measures were concurrent changes in maternal characteristics, including maternal age, nationality, place of residence, region of residence and parity.ResultsBetween 2014 and 2024, Georgia’s TFR declined from 2.30 to 1.68 children per woman (APC=3.12%, 95% CI −3.65 to −2.59), and the CBR dropped from 16.3 to 10.7 births per 1000 population per year (APC=4.39%, 95% CI −4.63 to −4.15). Fertility rates decreased most significantly among women aged <20 (APC=−9.93%, 95% CI −10.72 to −9.13) and 20–24 years (APC=−6.41%, 95% CI −7.70 to −5.10). Advanced maternal age increased in both nulliparous (APC=3.65%, 95% CI 1.83 to 5.51) and multiparous women (APC=1.67%, 95% CI 1.12 to 2.22). ARIMA forecast based on current trends showed a continued decline in the TFR to 1.09 (95% CI 0.91 to 1.27) children per woman and the CBR to 7.07 (95% CI 5.97 to 8.16) births per 1000 population by 2034.ConclusionsGeorgia is undergoing a demographic transition characterised by declining fertility rates and delayed childbearing. Assuming the mechanisms underlying 2014–2024 data remain unchanged, projections indicate a continued decline in fertility and birth rates by 2034, which may pose significant social and economic challenges for the country. Further research is needed to explore the underlying factors driving these trends and to develop strategies to address the potential implications of this demographic shift.

A major decline in the fertility of de14 veloping countries has occurred in recent decades. This article uses United Nations (UN) data to review the course of the fertility transition since the mid-1960s for less developed countries as a whole, for major regions and individual large countries.* We then examine some specific contributions of the Demographic and Health Surveys (DHS) toward defining and understanding the recent path of fertility decline. Table 1 shows total fertility rates (TFRs) for various regions of the developing world as calculated over five-year periods, and the decline in those rates. As indicated, the overall TFR for less developed countries declined by 30% in the 15 years between the period ending in mid-1970 and the period ending in mid-1985.t The decline was actually 47% of the difference between the TFR in the earlier survey period (6.01) and the target replacement level fertility (2.1).4 In 15 years, the world's less developed countries as a whole moved almost halfway toward the fertility level that is the eventual goal for most of them. Calculated on this basis, the decline was no less than 21% in any region, except in SubSaharan Africa. The TFR for less developed countries as a whole was estimated to have fallen only 3% in the 15 years before the 1965-1970 period, but 10% in the five years between the 1965-1970 and 1970-1975 periods,1 supporting the observation that the onset of sustained fertility decline only began in the late 1960s. A 47% overall decline toward replacement-level fertility in 15 years may be considered grossly inadequate by those who feel that more rapid fertility declines are badly needed in these less developed countries. While we do not deny that more rapid fertility declines would be desirable, we view the declines that have occurred as substantial-even remarkable in historical perspective-in view of several considerations. First, few observers were predicting such declines in 1965. The medium projection in 1968 assessment by the UN Population Division provided for a 15% decline in the TFR for less developed countries between the periods of 19651970 and 1980-1985.2 As we report, the decline was 30%. Projections underestimated fertility decline largely because the rates of change in social and economic development and the growth of effective family planning programs that occurred in those countries in which fertility declined were generally unexpected. Second, these declines required massive changes in reproductive behavior believed to be deeply rooted in traditional values and familial institutions. Third, family planning programs were only getting under way in 1965-1970, and, where they were in place, they were in their infancy. In 1960, only India had a national program, and it was proving ineffective. For a major fertility decline in these countries to occur, the proportion of married women of childbearing age using contraception had to increase-as it did-from

1970-79 US fertility trends among differnet racial, regional, age, educational, parity, and socioeconomic subgroups in the population were examined, using own children data from the 1976 Survey of Income and Education (SIE) and the March Current Population Surveys (CPS) from 1968-80. In addition, cross-sectional differences in fertility for the subgroups were compared for 1970 and 1976, using multiple regression analysis. 1st, the appropriateness of using fertility rates obtained from own children data was assessed by comparing fertility rates obtained from the SIE data with those derived from vital statistic and census data. The comparative analysis confirmed that the SIE data yielded an accurate estimate of period fertility rates for currently married women, provided the subgroup samples were sufficiently large. CPS fertility estimates were also judged to be accurate if data from 3 adjacent survey years was pooled to increase sample size. Fertility trends for 5 educational groups were assessed separately for 1967-73. During this periold, there was a marked decline in fertility for all 5 groups; for the group with 5-8 years of education the decline was only 14%, but for the other 4 groups, which included women with 9-16 or more years of education, the decline in fertility ranged from 26-29%. In assessing the 1970-76 trends, the sample was restricted to own children, aged 3 years or less, of currently married women, under 40 years of age. Among whites, there was an overall 20% decline in fertility between 1970-76 and an overall fertility increase of about 2% between 1976-79. These trends were observed in all 28 white subgroups. A similar pattern was observed for blacks. There was an overall fertility decline of 24% between 1970-76, and this decline was apparent for all subgroups except women with college degrees. Betwen 1976-79, black fertility rates, unlike white rates, continued to decline, but the rate of decline was only 3%. Furthermore, the decline in almost all the black subgroups was markedly less than in the 1970-76 periold, and for many of the subgroups the trend was reversed and fertility increased. In summary, the fertility trends noted for 1970-79 were pervasive for almost all the subgroups for both blacks and whites; i.e., there was a marked decline in fertility between 1970-76 and than a reversal or slowing down of the decline during the 1976-79 for all black and white subgroups. Cross-sectional fertility differences in the subgroups in 1970 and in 1979 were quite similar, and fertility rates differed markedly for the separate subgroups. These differences do not, of course, explain the pervasive trends observed in the analysis of the fertility rates over time. A similar study assessing fertility trends among subgroups for the early 1940's through the late 1960s also revealed the pervasive nature of period fertility trends. Demographers have not as yet been able to explain these shifts in fertility that cut across all subgroups in the US and which also characterize the period fertility rates in other developed countries. Tables provided information on 1) total fertility rates by educational level and by geographical region for 1945-1975; 2) % change in number of own children less than 3 years of age among women under age 40 by maternal age, maternal education, initial parity, geographical region, and husband's income; and 3) mean number of own children less than 3 years of age among women under age 40 by maternal age, education, parity, region, and husband's income.

This time-series analysis examines during 1860-1910 the impact of changes in local prices of agricultural commodities on wages among men and women in Sweden and the impact of changes in wages on fertility decline child mortality urbanization and improved social conditions. Linear ordinary least squares models are set up to determine whether demand-induced increases in womens wages relative to mens are significant factors in explaining Swedish total fertility rates. Data are obtained from 25 counties and the city of Stockholm among pooled observations for six time periods during 1860-64 and 1910-14. This period is marked by a total fertility decline of 28% a marital fertility decline of 26% and a child mortality decline of 52%. Explanatory variables are the relative prices of main agricultural outputs employment outside of agriculture the proportion of urban population and the child mortality rate. Findings indicate a significant relationship between child mortality and total fertility and specific fertility rates among women aged 20-39 years. A doubling of the male real wage rate was significantly associated with earlier marriage higher birth rates among women aged 15-29 years and lower birth rates among women aged 35-49 years. The ratio of male wages to female wages was associated with a decline in birth rates at all ages with the exception of teenagers. 66% of fertility increase was due to declines in child mortality but this increase was counterbalanced by fertility decline among older women. Child mortality and urbanization depressed marital fertility. Male wages increased the proportion married. A 10% increase in the male/female wage rate was associated with a 25% reduction in the total fertility rate. Another 25% increase in the male/female wage rate was associated with a 50% reduction in child mortality. It is concluded that the value of womens time had a key impact on the Swedish fertility transition.

Recently, there has been worldwide growing interest on profiling the human fertility of populations because there has been a noticeable global decline in fertility rate, leading to increased attention toward reproductive health and fertility.The decline in fertility of population of the Arab World was investigated for the 10 years period between 2011–2021. The Arab World was classified into three regional blocks; Block-1 Arabian Peninsula countries: Bahrain, Kuwait, Saudi Arabia, Oman, Qatar, United Arab Emirates (UAE), Yemen. Block-2 Fertile Crescent Arab countries: Iraq, Jordan, Lebanon, Syria, West Bank and Gaza. Block-3 African Arab countries: Algeria, Comoros, Djibouti, Egypt, Libya, Mauritania, Morocco, Somalia, Sudan, Tunisia. Data on fertility rates for the 10 years period between 2011–2021 were collected from the World Bank for Arab countries. Statistical analysis along with decline in the fertility rates were determined. Results: Fertility rates varied across Arab countries in 2011 and 2021, with notable decline ranging from 24.3% to 3.8%, except for Algeria, with zero decline. Countries that exhibited significant decline were Jordan (24.3%) followed by Iraq (22.2%) then Yemen (19.1%); Whereas, countries that exhibited slight fertility decline were Libya (3.8%), followed by Tunisia (4.5%), Lebanon (4.5%) and Kuwait (4.5%). On another note, lowest fertility rate was observed in UAE as maintained between 1.7% and 1.5% and the highest fertility rate was observed in Somalia as maintained between 7.3% and 6.3% for 2011 and 2021 respectively. Conclusion: The present study reveals the declining-trend in fertility rate across Arab countries, influenced by variable factors. Therefore, we recommend to the Council of the Health Ministries in the Arab-League to focus on investigating the fertility decline as an important parameter for public health in the Arab world to maintain natural balanced fertility rate.As some non-biological factors surrounding the Arabian region, such as instability, war, migration, the present study did not aim to include the influence of war and migration on fertility because both war and migration are non-biological external factors and both are not among the WHO criteria for fertility determination which based of the population growth rate of population under normal living conditions.

This article summarizes Larsens findings based on an analysis of data from the 1973 Tanzania Demographic Survey and the 1991-92 Tanzania Demographic and Health Survey on fertility and subfertility. The rates of childlessness in Tanzania declined among women aged 30-49 years from 10-11% to 3%. The effects of infertility on the total fertility rate (TFR) varied among Tanzanias 20 regions. In regions where TFR was over 6.8 children per woman in 1973 fertility declined. The range in decline was from 25% in Mbeya to 6% in Kagera. Fertility also declined in some regions with low contraceptive prevalence. Fertility declined 3% in Mara and 25% in Kilimanjaro. In most regions TFR remained the same or increased even in the three regions with increased contraceptive prevalence. Larsen suggests that these changes may be due to decreases in childlessness and subfertility. Three regions with low fertility in 1973 experienced further fertility decline in 1991-92. In Dar es Salaam TFR was 4.7 in 1973 and 4.1 in 1991-92 and contraceptive prevalence was 11% in 1973. In Mtwara TFR declined from 5.2 to 3.9 and childlessness and infertility were among the highest in 1973. Larsen suggests that fertility decline in these regions may be due to high rates of sexually transmitted diseases (STDs). In regions with both fertility decline and high contraceptive prevalence fertility transition may be underway. Larsen mentions that rural levels of childlessness declined over time and that variations between regions may be due to variability in STD prevalence. Declines in childlessness and subfertility may be due to improvements in malaria and STD treatment and treatment of pregnancy complications. Childlessness among urban women declined from 14% to 5%. In 1973 childlessness was high in the Tabora plateau lake basin and coastal regions. By 1991-92 subfertility in coastal regions remained high and actually increased among women aged 25-29 years.