Fertility in High-Income Countries: Trends, Patterns, Determinants, and Consequences

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)

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 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.

Brazilian fitness programme registers health benefits

Japanese society faces a low fertility rate and population decline, and the declining population brings about a shortage of labor forces. Therefore, female labor participation is attracting attention. However, cross-sectional data show a positive relationship between the total fertility rate (TFR) and female labor participation in recent years, while the time series data suggest a negative relationship. This may be a puzzle waiting to be solved. This chapter empirically interprets the dynamic relationship between the TFR, female wage, and labor participation from the time series perspective, using Japan’s experience. Generally, the VAR model is used to confirm the dynamic relationship among relevant variables; however, it is not easy to use the VAR model because the variables have a unit root. This study verifies the dynamic relationship using the error correction model (ECM) and Bayesian vector autoregression (BVAR) models. Therefore, both ECM and BVAR models show a similar result. They show that female wages negatively impact the fertility rate, and a similar result was obtained from both ECM and BVAR models. Women’s wages are an opportunity cost of having children, and we reconfirmed this fact in Japan’s experience. Additionally, this means that the compatible situation for women between labor provision and rearing children has been insufficient in Japan. Furthermore, adding the government benefits to young families to the BVAR model shows that this positively affects the TFR by calculating impulse response function. This can be interpreted as the government benefits reducing the opportunity cost of children. Hence, the intervention by the government to improve the TFR is appropriate.

This article aims to study the medium-term fertility trend in Latvia answering the following research question: is it possible to increase fertility in the near future, as planned in the “Population Reproduction Strategy FAMILY – LATVIA – 2030 (2050)”? The authors analyzed the total fertility rate (TFR) in Latvia for the period of 1970–2022 (53 years). This interdisciplinary (demography, mathematics, economics, sociology) study applies mathematical analysis for investigating socio-demographic trends, as well as the theory of economic cycles for identifying demographic cycles and their phases in Latvia and forecasting fertility rate in the near future. Furthermore, the analysis of data from comparative sociological surveys helped the authors to understand the main reason for the decline in fertility in Latvia. This reason is value changes in society, in which the child is no longer at the center of the value system of men and especially women in Latvia. In turn, short-term ups and downs in fertility are determined mainly by socio-economic and political factors. Based on the results of differentiating the function of the total fertility rate (TFR) it can be expected that the decline in fertility in Latvia will continue for several more years before the bottom of the next demographic cycle is reached and there will be an upturn in a linearly declining fertility trend. Although, the upcoming bottom will be lower than the previous one, i.e. below 1.22–1.25 children on average per woman and the next peak will most likely be below 1.74. Thus, the increase in TFR in Latvia to 1.77 by 2027, planned in the “Population Reproduction Strategy,” is considered by the authors of this study to be absolutely unattainable.

A Bedouin-origin clan with extremely high fertility in south Jordan sedentarized in a village in 1948. In this paper, based on our genealogical-demographic data collected from this clan, we elucidate the long-term demographic change that this clan has undergone. Our results highlight differences in fertility indicators among the early (1950–1969), middle (1970–1989), and late (1990–2004) periods. In particular, the total marital fertility rate (TMFR) was 4.756, 9.852, and 9.146, respectively, and the total fertility rate (TFR) was 3.589, 7.214, and 5.189, respectively. Taking age-specific indicators into account, the increase in TFR or TMFR from the early to the middle periods was attributable to increased rates of childbirth among middle-aged and older women, presumably associated with improvements in maternal nutrition and increased demand for children as agriculture labor, while the decrease in TFR from the middle to the late periods was caused by a delay in females’ age at marriage and an increase in the number of unmarried females. The fertility of this clan has been high, especially in the late period, and it is estimated that its population will double by 2030, implying the urgent necessity to reduce fertility to prevent overpopulation that threatens the people’s subsistence adaptation.

The economic implications of population aging on current account balance

Reproductive health inequalities tend to be more marked in large cities because they include neighbourhoods with unequal social and community networks, resources and opportunities. The aim was to describe social inequalities in fertility patterns among women who gave birth between 2007 and 2016 in the city of Barcelona (Spain) by jointly evaluating the effect of individual and socioeconomic neighbourhood characteristics. We conducted a multilevel study of women's individual characteristics (age, educational attainment, and country of origin) and neighbourhood characteristics (disposable household income, percentage of unemployment and percentage of foreigners). Multilevel modelling revealed significant variability in fertility across neighbourhoods after adjustment for individual explanatory variables. The fertility rate (FR) was higher in better educated women (adjusted relative risk: aRR 2.76), those aged 30-39years (aRR 2.13), and in those born in Spain in relation to their respective reference groups. The FR was lowest in women with upper to post-secondary, non-tertiary education (aRR 0.86) who were born in high income countries (aRR 0.57). Women living in neighbourhoods with a low income (aRR 1.46) and with a higher unemployment (aRR 1.33) were more likely to have children. In contrast, women living in neighbourhoods with a lower percentage of foreigners had the lowest FR (aRR 0.81). The neighbourhood's characteristics played an important role in fertility patterns, independently of women's individual characteristics. At the contextual level, the highest FR was found in the poorest settings, highlighting inequalities. At the individual level, the FR was highest among women with higher education and in their thirties.

While spoken about spatial distribution of total fertility rates (TFR), generally attention is paid to changes in level of (total) fertility rates and/or birth rates. In the paper attention is paid to stability of ranking of Polish regions defined in terms of TFR values, and to changes in fertility distribution among regions with extreme TFR levels. Age groups with the largest fertility differences are sought. Despite high liability group of ”losers” could be found (regions with low TFR – Opolskie and Dolnośląskie Voivodships). In spite of fact that fertility distribution are becoming more and more similar, growing differences are observed when analyzed fertility of teenagers and females aged 35+.

Materials and methods. The source of information on fertility and mortality was Russian Federal State Statis-tics Service data on 173 cities of the Russian Federation with a population of more than 100 thousand peo-ple for 2014-2016. The sources of air pollution were the Yearbooks of the state of air pollution in cities in Russia for the same period. A nonparametric analysis of variance was used to compare cities with different levels of air pollution in terms of fertility, mortality, birth-death and aging index. Results. Comparison of groups of cities with different maximum one-time levels of atmospheric pollution with benzo[a]pyrene revealed significant differences between the groups in terms of total fertility and mortal-ity rates, birth-death ratio and aging index. Comparison of groups of cities with different average annual levels of air pollution for 19 pollutants showed that only for benzo[a]pyrene, nitrogen dioxide, nitric oxide and ammonia a statistically significant relation-ship with the characteristics of population health was found. For benzo[a]pyrene, the results were similar to those obtained for the maximum one-time concentrations of benzo[a]pyrene (except for the total mortality rate, for which the relationships are not significant). Statistically significant relationships of air pollution with nitrogen dioxide, nitrogen oxide and ammonia were revealed with the total mortality rate and the birth-death ratio, and for ammonia pollution also with the demographic aging index. Conclusion. In cities of the Russian Federation, the maximum one-time air pollution with benzo[a]pyrene is statistically significantly associated with the total fertility rate, birth-death ratio and the aging index: in groups of cities with an increase in the level of atmospheric air pollution, the median values of the total fer-tility rate and the birth-death ratio are increasing, and the median values of the aging index are decreasing. Comparison of groups of cities with different average annual levels of air pollution for 19 pollutants re-vealed statistically significant effects for pollution with benzo[a]pyrene, nitrogen dioxide, nitrogen oxide and ammonia. A statistically significant heterogeneity of the groups of cities with different average annual levels of benzo[a]pyrene was revealed in terms of the total fertility rate, birth-death ratio and aging index: with an increase in the level of atmospheric air pollution, the median values of the total fertility rate and the birth-death ratio increase, while the median values of the aging index decrease. A statistically significant hetero-geneity of the groups of cities with different average annual levels of air pollution with nitrogen dioxide, ni-tric oxide and ammonia was revealed in terms of the total mortality rate and the birth-death ratio, and for ammonia pollution also in terms of the demographic aging index. In cities with a high level of air pollution with nitrogen dioxide and ammonia, the total mortality rate is higher.

Introduction:HIV reduces fertility through biological and social pathways, and antiretroviral treatment (ART) can ameliorate these effects. In northern Malawi, ART has been available since 2007 and lifelong ART is offered to all pregnant or breastfeeding HIV-positive women.Methods:Using data from the Karonga Health and Demographic Surveillance Site in Malawi from 2005 to 2014, we used total and age-specific fertility rates and Cox regression to assess associations between HIV and ART use and fertility. We also assessed temporal trends in in utero and breastfeeding HIV and ART exposure among live births.Results:From 2005 to 2014, there were 13,583 live births during approximately 78,000 person years of follow-up of women aged 15–49 years. The total fertility rate in HIV-negative women decreased from 6.1 [95% confidence interval (CI): 5.5 to 6.8] in 2005–2006 to 5.1 (4.8–5.5) in 2011–2014. In HIV-positive women, the total fertility rate was more stable, although lower, at 4.4 (3.2–6.1) in 2011–2014. In 2011–2014, compared with HIV-negative women, the adjusted (age, marital status, and education) hazard ratio was 0.7 (95% CI: 0.6 to 0.9) and 0.8 (95% CI: 0.6 to 1.0) for women on ART for at least 9 months and not (yet) on ART, respectively. The crude fertility rate increased with duration on ART up to 3 years before declining. The proportion of HIV-exposed infants decreased, but the proportion of ART-exposed infants increased from 2.4% in 2007–2010 to 3.5% in 2011–2014.Conclusions:Fertility rates in HIV-positive women are stable in the context of generally decreasing fertility. Despite a decrease in HIV-exposed infants, there has been an increase in ART-exposed infants.

Population aging is an inevitable global trend, driven by increased life expectancy and a prolonged decline in the total fertility rate (TFR) below replacement level. This article analyzes the current situation and underlying causes of persistently low fertility in high-income countries and in Vietnam. It points out that once the TFR drops below 2.1 for several decades, almost no country has succeeded in restoring replacement-level fertility, despite implementing numerous supportive policies at great cost. Through the analysis of demographic, economic, and social data from over 40 countries, combined with typical surveys conducted in Ho Chi Minh City and Japan, the author identifies five root causes of fertility decline. Based on these insights, a comprehensive system of solutions is proposed to shift from unsustainable to sustainable aging. Key measures include: ensuring a living wage for a family of four; reforming the social insurance system; expanding public housing and preschool education; promoting gender equality; preserving Vietnamese cultural traditions that value family life; educating citizens on knowledge and skills for building happy families; raising awareness of civic responsibility; and creating a social environment that is friendly to women and children.The article emphasizes that without decisive policy changes during the 2025–2050 period, Vietnam’s population could shrink from a peak of 107 million in 2050 to just 88 million by 2100, with further severe decline thereafter. Conversely, if strategic solutions are comprehensively implemented, Vietnam can fully achieve the goal: happy elderly, a prosperous nation, and a thriving people.

The Brazilian period total fertility rate (PTFR) dropped to 1.8 in 2010 (1.5 among those with high education). Due to shifts in fertility timing, the PTFR may provide a misleading picture of fertility levels. The consequences of these changes for the cohort total fertility rate (CTFR)—a measure free from tempo distortions—and for educational differences in completed fertility remain unknown. Due to data limitations, CTFR forecasts in low- and middle-income countries are rare. We use Brazilian censuses to reconstruct fertility rates indirectly and forecast the CTFR for all women and by educational level. Four forecasting methods indicate that the CTFR is unlikely to fall to the level of the PTFR. Educational differences in the CTFR are likely to be stark, at 0.7–0.9, larger than in many high-income countries with comparable CTFRs. We show how the CTFR can be forecasted in settings with limited data and call for more research on educational differences in completed fertility in low- and middle-income countries.

Purpose Declining birth rates are an important social issue worldwide, and allocating resources to benefits for child-rearing households is a necessary countermeasure. In this study, we investigate and compare the relationship between child-related social security costs and total fertility rate internationally.Method In 2011, the ratios of family- and elderly-related social spending to gross domestic product (GDP) were recorded in 34 Organisation for Economic Co-operation and Development (OECD) member countries. The former was designated as the percentage allocated to members of the population between the ages of 0 and 14 (young population ratio), and the latter was designated as the percentage allocated to members of the population over the age of 65 (elderly population ratio). We compared these two factors for every country and performed correlation and partial correlation analyses of total fertility rates and family-related social spending to GDP adjusted by population proportion to examine the relationship between the two.Results The comparison of the youth and elderly populations' spending ratios in the total social spending to GDP ratio revealed that OECD countries had an average family-related social spending ratio of 0.13 and an average elderly-related social spending ratio of 0.47. Comparatively, Japan has a family-related social spending ratio of 0.10 and an elderly-related social spending ratio of 0.45. In the correlation analysis for total fertility rates and family-related social spending to GDP ratio, the benefits-in-kind to GDP ratio and total fertility rates indicated a trend toward correlation (r=0.32, P=0.06). In addition, the results for the partial correlation between family-related social spending to GDP ratio and total fertility rates showed a significant correlation between the two. When divided into cash benefits and benefits-in-kind, the partial correlation analysis of the GDP ratios and total fertility rates revealed a significant correlation (r=0.51, P<0.01). Benefits-in-kind (r=0.45, P<0.01) had a stronger correlation than did cash benefits (r=0.39, P=0.03).Conclusion In an international comparison accounting for economic standards, the social security cost of children and child-rearing families in Japan was low. Further, in terms of social security spending, increasing benefits-in-kind, such as improving childcare services or preschool education, is one factor that improves the total fertility rate.