Religions by Continent

To supplement our global assessment of 18 religions in a previous article in this issue of the journal, here we offer analysis of religious affiliation by the globe and six continents: Africa, Asia, Europe, Latin America, Northern America, and Oceania. Estimates of religious affiliation are made for four dates, 1970, 2000, 2022, and projections for 2030. We also contrast growth in two 30-year periods, 1970–2000 and 2000–2030. These global and continental tables are based on country data (World Religion Database). United Nations regional tables (e.g., Western Africa) can also be constructed from the country data.

January 2010 T World Religion Database (WRD) is part of a most impressive data-collection project, requiring an extraordinary number of hours to create. The WRD can and will be improved over time, but we can only thank the editors now for their extremely valuable service, including their work of overseeing hundreds of people behind the scenes gathering the data. Although based on the World Christian Encyclopedia (WCE, 1982; 2d ed., 2001), WRD goes beyond it in several important ways. First, for those interested in statistical research, WRD data are downloadable as Excel® files. Second, for many countries in the data set, WRD lists censuses and surveys that give alternate estimates of religious distribution. This is extremely helpful, since it allows scholars to compare WRD estimates with those of others and to evaluate the quality of data used to estimate religious distribution in particular countries. Third, WRD provides data on more countries, regions, and time periods than does any other source. Fourth, WRD provides incredibly detailed data. Previous versions had data at the national level, but WRD presents it World Religion Database: Impressive—but Improvable

This article offers analysis of religious affiliation for 18 categories of religion for the globe and six continents: Africa, Asia, Europe, Latin America, Northern America, and Oceania. Estimates of religious affiliation are made for four dates, 1970, 2000, 2020, and projections for 2030. Annual average growth rates are provided for two 30-year periods, 1970–2000 and 2000–2030. These global and continental tables are aggregated from country data in the World Religion Database.

This article offers analysis of religious affiliation for 18 categories of religion for the globe and six continents: Africa, Asia, Europe, Latin America, Northern America, and Oceania. Estimates of religious affilia¬tion are made for four dates, 1970, 2000, 2018, and projections for 2030. Annual average growth rates are provided for two 30-year periods, 1970–2000 and 2000–2030. These global and continen¬tal tables are aggregated from country data in the World Religion Database.

International Bulletin of Missionary Research, Vol. 34, No. 1 T World Religion Database (WRD) is exactly what its name implies—it covers every country of the world, it focuses on religions, and it is a most incredible database. The amount of work that has gone into producing such a prodigious assembly of facts about every country is enormous, and the editors must be thanked for their diligence, perseverance, and sheer dedication to a mammoth project that can only become more and more useful as time goes by, assuming it is kept up-to-date with the same diligence and resources that have gone into its initial framing. The WRD is based on David Barrett’s World Christian Encyclopedia (WCE; Oxford Univ. Press, 1982; 2d ed., 2001). It exceeds the WCE, having been updated and extended in many useful ways. Todd Johnson, the lead editor of WRD, has done a brilliant job in making the WCE accessible in a modern format and deserves huge plaudits for so doing. The WRD is a truly remarkable resource for researchers, Christian workers, church leaders, religious academics, and any others wanting to see how the various religions of the world impact both the global and the local scenes. It is always easy to criticize any grand compilation of statistical material by looking at the detail in one particular corner and declaring, “That number doesn’t seem right.” The sheer scope of this database, however, is incredible, and the fact that it exists and can be extended even further and updated as time goes forward in the framework of a respected university deserves huge applause for those responsible for it. Praise where praise is due, even if I am about to critique it. Most researchers coming to a world database would presumably look first at their own nation. Immediately a problem—mine is not listed. There is no United Kingdom (UK) in the WRD. The UK is composed of four countries, but they are not separately listed either, so I cannot look up, say, England. We sometimes talk of Great Britain, but that is not listed either. Ah, I have it—we are called Britain in the WRD. Why? That is not our name. America is not listed under “America” (despite what many people call it) but under its proper title of United States. Why is the UK treated differently? World Religion Database: Detail Beyond Belief!

BackgroundNinety-eight percent of documented cases of the zoonotic disease human monkeypox (MPX) were reported after 2001, with especially dramatic global spread in 2022. This longitudinal study aimed to assess spatiotemporal risk factors of MPX infection and predict global epidemiological trends.MethodTwenty-one potential risk factors were evaluated by correlation-based network analysis and multivariate regression. Country-level risk was assessed using a modified Susceptible-Exposed-Infectious-Removed (SEIR) model and a risk-factor-driven k-means clustering analysis.ResultsBetween historical cases and the 2022 outbreak, MPX infection risk factors changed from relatively simple [human immunodeficiency virus (HIV) infection and population density] to multiple [human mobility, population of men who have sex with men, coronavirus disease 2019 (COVID-19) infection, and socioeconomic factors], with human mobility in the context of COVID-19 being especially key. The 141 included countries classified into three risk clusters: 24 high-risk countries mainly in West Europe and Northern America, 70 medium-risk countries mainly in Latin America and Asia, and 47 low-risk countries mainly in Africa and South Asia. The modified SEIR model predicted declining transmission rates, with basic reproduction numbers ranging 1.61–7.84 in the early stage and 0.70–4.13 in the current stage. The estimated cumulative cases in Northern and Latin America may overtake the number in Europe in autumn 2022.ConclusionsIn the current outbreak, risk factors for MPX infection have changed and expanded. Forecasts of epidemiological trends from our modified SEIR models suggest that Northern America and Latin America are at greater risk of MPX infection in the future.

Background and Aims Fluid management is a fundamental component of dialysis care. Achieving euvolemia and avoiding fluid overload can be challenging in dialysis. Apollo Dial DB, an anonymised dialysis database from a global kidney care network, was used to gain deeper insights in patterns of patient body mass and fluid balance during dialysis treatments worldwide. Method Apollo Dial DB, a global anonymised dialysis database, contains real-world data from patients in 40 countries beginning January 2018 throughout March 2021. Parameters include demographics, diagnoses, laboratories, medications, treatments, quality of life, and outcomes. This analysis assessed body mass and fluid status parameters on patients in Asia-Pacific (AP), Europe, Middle East, and Africa (EMEA), Latin America (LA), Northern America (NA). In addition, bioimpedance for body compositions of patients was assessed as it was available in AP, EMEA, and LA. Results The first version of the Apollo Dial DB includes data on 543, 169 patients, with 4.6% from AP, 13.9% from EMEA, 7.0% from LA, and 74.5% from NA countries. EMEA and NA have a higher proportion of patients in the bigger height categories versus the AP and LA regions (Table 1). There is a difference in mean body weight post dialysis of more than 10 kg between regions; the highest weights were observed in EMEA and NA in contrast to the AP and LA regions. Average albumin levels were consistent in all global regions. Intradialytic weight gain is lowest in EMEA at 1.8 kg and highest in NA at 2.2 kg between treatments. While ultrafiltration volume ranged from 2.0 L to 2.4 L, being the highest in AP and NA. Bioimpedance measures defining body compositions were available for the majority of patients in AP (59.3%), EMEA (86.9%), and LA (90.7%). Patient averages in EMEA are slightly higher for ATM, FTM, LTM, TBW than for LA and AP; this is somewhat explained by the differences in body weight between the cohorts (Fig. 1). Conclusion A descriptive analysis of the body mass and fluid status across minor and major regions bring to light key profiles and differences in the characteristics of dialysis patients across the world and fluid management practices in dialysis care. Differences in intradialytic weight gain may deserve further analysis considering the impact of dialysis modality on fluid balance, such as potential influences of convective therapies. These findings act as benchmarks for the nephrology community. Apollo Dial DB offers opportunities for investigators to conduct global analytics and advance kidney disease research.

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

The Atlas of Global Christianity is a thorough visual reference of the changing status of global Christianity over the 100 years since the epoch-making 'Edinburgh 1910' World Missionary Conference. It is the first scholarly atlas to depict the twentieth-century shift of Christianity to the Global South. It is also the first to map Christian affiliation at the provincial level. The atlas is divided into five major parts: Part I covers the whole world with thematic maps on world issues and world religions comparing the global context of 1910 and 2010. It also contains maps on religious freedom and religious diversity. Part II focuses in on the Christian context with thematic maps on major Christian traditions including Anglicans, Independents, Marginals, Orthodox, Protestants and Roman Catholics as well as Evangelicals and Pentecostals. Part III depicts Christianity by the 21 United Nations regions (Eastern Africa, Western Africa, Southern Africa, etc). Each region is described in four pages including an historical essay, maps, graphs, tables and charts. In addition, an essay and maps are included for each of the six United Nations continental areas (Africa, Asia, Europe, Latin America, Northern America, and Oceania). Part IV views the world through languages, peoples and cities, a new area of scholarly analysis of Christianity and its resources. Part V focuses on Christian mission by analysing data on missionaries, finance, Bible translation, media broadcasting, and other forms of evangelisation. In the back sleeve, a CD with an interactive presentation assistant is included. It contains presentation-ready maps, charts, graphs and tables for classroom use. Key Features * First scholarly atlas to document the shift of Christianity to the Global South * Contextual maps of world issues and major religious traditions * Global coverage of religious freedom and religious diversity * First atlas to map Christian affiliation at the provincial level * Ecumenical and global coverage, including all Christian traditions in every country * Full-colour maps of Christian affiliation in every United Nations region in the world * Historical essays on Christianity 1910-2010 by scholars from each region of the world * Interactive presentation assistant on CD of all maps and graphics for classroom use

Over the last 40 years, the question of the “African exception” has regularly come to the forefront in the discussion of fertility trends. In the 1980s, there was uncertainty about when fertility decline would commence throughout the region: while fertility was declining at a steady pace in Latin America and Asia, decline was evident in only a minority of sub-Saharan countries and, indeed, some countries showed fertility increase. As of the 1990s there was evidence of fertility decline in most countries of sub-Saharan Africa, and it appeared that sub-Saharan Africa was following the historical pattern of the other major regions. With a slow pace of fertility decline, or even stagnation at relatively high levels in various countries (Bongaarts 2008), the question of Africa's exceptionality has resurfaced. The fertility level in sub-Saharan Africa is the world's highest (5.1 children per woman versus 2.2 in Latin America and Asia in 2010–15; United Nations 2015a) (see Table 1). Compared with the experience of other world regions, sub-Saharan Africa stands apart not only in terms of fertility levels, but also with regard to a flatter age pattern due to longer birth intervals, the persistence of high ideal family size, and a low level of contraceptive use (Bongaarts and Casterline 2013). Sub-Saharan Africa also deviates from standard international patterns in terms of nuptiality. The traditional nuptiality regime has been defined by a particular combination of features, both for first marriage (early marriage for girls, a large age gap between spouses, almost universal marriage for both sexes) and for later conjugal life (polygamy, prompt and widespread remarriage for widowed and divorced women of childbearing age) (Lesthaeghe et al. 1989; United Nations 1988, 1990; van de Walle 1968). This dominant pattern existed with geographical differences, however, and also exceptions (especially Southern African countries). It has also been affected by significant changes over recent decades, especially through the increase in women's age at first union (Antoine 2006; Garenne 2004; Hertrich 2007; Lloyd 2005; Mensch, Grant, and Blanc 2006; Mensch, Singh, and Casterline 2005; Ortega 2014; Shapiro and Gebreselassie 2014; Westoff 2003), a narrowing gap between male and female age at marriage, and recent evidence of polygyny decline in Western Africa (Antoine and Marcoux 2014; Hertrich 2006). Despite these trends, sub-Saharan Africa still stands out in international comparisons both for the youngest age at first union for women and the largest age difference between spouses at first union (see Table 1). In 2010, the median age at first union was 21.2 years for women in sub-Saharan Africa, 1.4 years earlier than in Asia, and much earlier than in other parts of the world (25 to 28 years). Except in Southern Africa, the pattern is even earlier (20.3–20.5) at subregional levels, but it has an equivalent counterpart in the subregion of South Asia. The difference in age at union between males and females also remains significantly higher in Africa (5.5 years on average) than in the rest of the world, where the regional average is around 3 years or less. To what extent are these sub-Saharan fertility and nuptiality patterns bound up with each other? Are nuptiality changes part of the fertility transition? Is fertility decline possible in a context of early marriage? Is there empirical evidence of changes in age at marriage before or at the onset of the fertility transition? In this chapter I adopt a comparative approach to examining long-term trends in female age at marriage and fertility in sub-Saharan Africa, with a focus on continental countries having at least 1 million inhabitants. My database on nuptiality includes over 360 censuses and national surveys conducted in these 39 countries since the 1960s. I analyze the association between changes in age at first union and the onset of fertility transition, examining whether there is a typical pattern of association followed by most countries in the region. Questions about nuptiality changes in connection to the fertility transition are present in the classical literature on the demographic transition. The issue is of particular interest in sub-Saharan Africa, where the traditional marriage regime strongly supports high fertility. The idea that nuptiality change is part of the demographic transition was conceptualized in the 1960s. In Kingsley Davis's “theory of change and response” (1963), the restriction of nuptiality (through increases in age at marriage and/or in permanent celibacy) is, like emigration or limitation of marital fertility, one of the “multiphasic responses” to the sustained natural increase generated by mortality decline. Postponement of marriage is not a deliberate effort to reduce fertility; but, along with migration, it has often been a first collective response to demographic pressure, as it is easier to adopt than a restriction on marital fertility (United Nations 1990). Ansley Coale (1967, 1974) subsequently distinguished two steps in the fertility transition: first a “Malthusian transition” in which general fertility is lowered by the restriction of marriages; and, second, a “neo-Malthusian transition” in which a decrease in marital fertility resulted from the deliberate choice of couples to limit the number of their children. This two-step approach was later adopted by Jean-Claude Chesnais (1986) in his extensive work on demographic transition. Based on his assessment of the countries where fertility declined before the 1980s (i.e. excluding sub-Saharan Africa) and despite exceptions in Latin America, he concluded that nuptiality transition could be considered as a first step in the fertility transition across a large part of the world: “In all countries where there is appropriate statistical information, control of marriages preceded birth control by couples” (ibid., p. 381). More broadly, the robustness of this theory relies on a two-stage process where nuptiality change is a prelude to deliberate birth control; it acts as a regulator of general fertility but can occur before the sustained fertility decline that fixes the onset of fertility transition.1 The pronatalist nature of the traditional African nuptiality system has been widely documented2 and can be summarized by two aspects. First, it maximizes the span of a woman's reproductive life that is assigned to reproduction. Unlike pre-transitional Europe, where late marriage and permanent celibacy restricted the potential of fertility, in sub-Saharan Africa the traditional fertility-inhibiting factors operate mainly within marriage by means of the postpartum infecundability that results from long breastfeeding and postpartum abstinence (Page and Lesthaeghe 1981). A woman's life course is structured by marriage and reproduction: she is married at a young age; and if the marriage ends (through divorce or widowhood), she quickly remarries—at least while she is still of childbearing age. In the 1980s, the proportion of reproductive time spent out of union was usually below 20 percent (United Nations 1986), with an average of around 15 percent (Bongaarts, Frank, and Lesthaeghe 1984) and values below 10 percent in various Western African populations. Polygyny3 is one of the keys to the smooth running of this system, as it makes the marriage market more flexible. Indeed, in case of marital disruption, a woman can remarry rapidly without waiting for a single partner to become available (Locoh 2006; Hertrich 2006). The second aspect of the association between the nuptiality and high-fertility regimes is related to the organization of the conjugal unit and of gender relations. Institutional arrangements converge to limit the conjugal unit to its reproductive tasks and to impede conjugal intimacy and autonomous decision-making. The traditional marriage system largely contributes to building weak relationships between spouses and, therefore, to hindering the elaboration of common and independent fertility decisions (Caldwell 1982; Lesthaeghe 1980; Lesthaeghe et al. 1989; Mason 1993; National Research Council 1993; Ryder 1983). In addition large age gaps between spouses creates a distance between them as a result of the generational and cultural gap between the partners and reinforces the subordinate position of the wife. Polygyny and the high risk of marital disruption are other causes of a frail conjugal bond, because they create uncertainty and a climate of distrust between spouses (Antoine 2006; Hertrich and Locoh 1999). The weakness of the conjugal bond is seen as enhancing fertility through different paths. First, couples have little incentive to question normative behaviors when there is little privacy and opportunity for discussion between spouses. Husbands and wives usually have separate budgets and therefore little opportunity to discuss the full costs of childrearing (all the more so given that the costs of children are often spread across a larger family network). Second, the frailty of conjugal bonds provides women a powerful rationale for high fertility. In rural patrilineal societies, where women have limited access to land and economic assets, having children is a critical means to securing access to household resources and to consolidating their status in relation to husbands, in-laws, and possible co-wives. According to a recent study (Lambert and Rossi 2016), high fertility remains a strategy for women in the face of uncertainties and family rivalries in Senegal. According to these considerations, it makes sense to anticipate that fertility transition requires—or at least would be facilitated by—a loosening of traditional marriage patterns. Depending on the analytical approach, one can expect changes in nuptiality and fertility trends to be either simultaneous or sequential. The first case (simultaneity) refers to a direct, mechanical effect of nuptiality on fertility. It is conceptualized through the framework of the proximate determinants of fertility. At the population level, other things being equal, a decline in the time spent in union (i.e., having a regular sexual life) will lower fertility. Modeling fertility by using a large body of international data has confirmed nuptiality as one of the four key proximate determinants of fertility (the three others being contraception, postpartum infertility, and abortion) (Bongaarts 1978, 1992). According to this outline, the inhibiting effect of delayed nuptiality increases, on average, in the first stage of fertility transition, but the impact of contraception becomes dominant and much stronger as the fertility transition progresses (Bongaarts 1992). In some regions, like North Africa in the 1970s and the 1980s, the postponement of marriage was a leading cause of fertility decline (Westoff 1992; Ouadah-Bedidi and Vallin 2000). In sub-Saharan Africa, the picture is more mixed. Data and studies are fragmentary concerning the impact of nuptiality on fertility at the outset of the transition. Country-level studies usually provide evidence of changes in the age at first union at the onset of fertility decline. For Eastern and Southern Africa, Harwood-Lejeune (2001) estimates that one-sixth to one-third of the fertility declines in the 1980s and early 1990s is explained by rising age at marriage. Two recent large-scale comparative studies (Garenne 2014; Shapiro and Gebreselassie 2014) conclude that delayed marriage in most countries in the region had a small impact on fertility decline when compared to the overwhelming contribution of contraception. However, these studies examine long periods of time (comparing the results from the most recent DHS to those from the first available one or to the estimates at the onset of fertility decline); therefore, the possible effect of nuptiality at the onset of fertility decline is difficult to capture and is probably underestimated because it is diluted over time and superseded by the impact of contraception. In the second approach, nuptiality changes first—that is, before and possibly as a precursor to fertility decline. This is a possible scenario if the mechanical inhibiting effect of delayed nuptiality on fertility is counterbalanced by other changes, for instance if there is an increase in marital fertility. Here, the possible link between nuptiality and fertility decline does not necessarily have to be understood in a deterministic way: a single factor (for instance, increases in level of education) may both raise the age at marriage and increase contraceptive uptake. The general assumption is that the delay between later marriage and fertility decline corresponds to a period of change in the context of reproduction, especially in terms of increased individual autonomy and, possibly, conjugal autonomy. This type of scenario (delayed age at marriage without simultaneous fertility decline) has been considered for Africa by Chojnacka (1993, 1995). The comparative analysis of long-term trends in age at marriage and fertility throughout sub-Saharan Africa presented below will provide the opportunity to examine the occurrence of both scenarios. The objective here is to describe historical trends in age at first union and fertility and to examine the temporal relationship between the two trends, especially during the period around the beginning of fertility decline. Tracing long-term demographic trends across sub-Saharan Africa is difficult. Although the availability of data has increased significantly since the 1980s, the situation was previously fragmentary. The quality of data and the comparability between sources are additional obstacles to obtaining consistent series. One usual solution is to limit the analysis to a single source (for instance, using retrospective data from one survey or several surveys from the same program, such as the DHS). By contrast, the approach used here seeks to take into account all available national censuses and surveys since 1950. The objective is to extend as far as possible the time span considered and to increase the robustness of the data by taking advantage of cross-validation between sources. The cost, however, is that the statistical series are disconnected from other kinds of indicators. For instance, while indicators on nuptiality and fertility from the DHS could be linked with indicators on contraception, education, etc. (since they are computed from the same databases), this is not possible with the present series because they are derived from different sources and further harmonized. For fertility I use TFR series from the UN World Population Prospects (WPP), which are provided in 5-year periods since 1950 (UN 2015a). These series, which have been constructed by taking into account multiple sources and varying methods of estimation (Alkema et al. 2011, 2012), are certainly the most reliable data on African fertility. Unlike in the case of fertility, there are no ready-to-use harmonized data series on nuptiality, and a specific database was constructed. The indicator used is the median age at first union for women.4 Both series are available at the national level only. Most of the analysis focuses on continental sub-Saharan Africa and countries with at least 1 million inhabitants in 2010, a total of 39 countries. To examine trends in age at marriage, I use statistical tables on marital status by sex and age from INED's pan-African database on nuptiality (Hertrich 2007; Hertrich and Lardoux 2014) (see Appendix5). For the 39 countries considered here, the database includes 362 national censuses and surveys carried out since 1950—9.3 per country on average. These data are extensive enough to trace long-term trends in age at marriage since at least the 1970s for 31 countries, and since the 1960s for 24 countries. For 31 countries, the trends can be followed up to at least 2010; for 3 countries the data end between 2001 and 2005. Period estimates of age at first union were calculated from these cross-sectional data on marital status by sex and age using the approach proposed by Hajnal (Hajnal 1953; United Nations 1984). The series of proportions of never-married individuals by age can be equated with that of a theoretical cohort and summarized by a standard indicator such as mean age or median age at first marriage. I use the median age at first union rather than the singulate mean age at marriage (SMAM), which is difficult to interpret when nuptiality is changing. In sub-Saharan Africa (with the exception of Southern Africa), where marriage is nearly universal, occurs at young ages for women, and is concentrated within a narrow age range, the median age at first union captures the current pattern of nuptiality, which is that of the young cohorts (aged 15–24 years) reaching the age at marriage at the time of the survey. Precise information on age at first marriage is difficult to obtain because people in many African countries do not have good knowledge of ages and dates, and also because African marriage is often a process (rather than an event) involving various ceremonies and stages, and this leads to varying interpretations of the timing of entry into union (van de Walle 1968; Meekers 1992; Hertrich and Locoh 1999; Antoine et al. 2009; Hertrich 2013). The issue is especially important when using retrospective data and this leads to a preference for cross-sectional indicators (Lesthaeghe 1989; van de Walle 1968, 1993). Yet, errors may also arise with period data—for instance, concerning the marital status of women who have uncertain or transitional marital status. There may also be errors of age reporting, depending on women's marital status (with age transfer toward younger ages for never-married women, and toward later ages for married women) (Pullum 2006). Such distortions may be further exacerbated by the design of the survey or census (criteria of eligibility, status of the respondent, more inclusive approach to conjugal union by surveys as compared to censuses, lower coverage of unmarried women by surveys, etc.). Systematic evaluation that estimates of median age at first union in sub-Saharan Africa to be underestimated by individual surveys when compared to census data (Hertrich and Lardoux To take into account these between census and survey trends in median age at marriage were for each country to obtain harmonized series (see series were computed by between the or survey For the related to periods with estimates were when in to an The data on fertility are given by period and the series of fertility were computed by between the were to one A countries have or trends in nuptiality. This is especially the case for the African and and, to a and For of I the trends in these countries but I them to be I to changes in age at first union to the onset of fertility transition, the of the onset of fertility transition is Two One is to that fertility transition has when and fertility decline is a common (Bongaarts and Casterline Casterline is to the onset of fertility at the the TFR a level 10 percent below its The other approach (Alkema et al. the of fertility to sustained fertility decline as the onset of fertility transition. I that these two the period when fertility decline The of TFR could be as the (the of or early of fertility decline. I the for the when the TFR is 10 percent below the As fertility decline is slow in many African countries, the time between the two is usually years on average (see Table and one that fertility transition is confirmed when TFR is 10 percent lower than the historical is the only country still in a The comparative work on trends in African nuptiality, on a of censuses and surveys, was carried out in the early 1980s by Lesthaeghe and by van de Walle It showed an increase in women's age at first union but on the of such trends. Over the last 20 years, a number of studies extensive and often limited to retrospective have provided additional evidence on the increase in women's age at marriage (Garenne 2004; Hertrich 2007; Lloyd 2005; Mensch, Grant, and Blanc 2006; Mensch, Singh, and Casterline 2005; Ortega 2014; Shapiro and Gebreselassie 2014; and 2004; Westoff My data the change in first marriage patterns across the The pattern of early female which was a of the sub-Saharan nuptiality system, has been 1 and the toward later age at marriage spread to the during the last In the an early marriage pattern was The median age was below in most countries, with the exception of Southern Africa, where late marriage was the and to a extent some countries from and Eastern In the part of the during the the age at first marriage for women to increase By only a minority of countries in Western Africa) still had a median age at first marriage Over the following decades, the increase spread to much of Western Africa, and the pattern was confirmed in other regions. In the the early marriage that had been dominant years before had The one exception by was The standard is a median age over years at the beginning of conjugal and the years in a large number of countries. in women's age at first union and total fertility, UN for fertility; database on African nuptiality for median age at first trends in women's median age at first by country database on African nuptiality. The delay in women's first union has been a the assumption of a decline in age at first marriage in countries had from retrospective data (Garenne this is not by the cross-sectional Indeed, the only in which data a decrease or are those with In terms of geographical Southern Africa stands both because late marriage was common years and because age at marriage to increase rapidly in most countries. Except for median age at first marriage in most countries years and in some it The nuptiality pattern in Southern Africa a combination of late marriage for both significant of people who small gender difference in ages at marriage, marital low and low levels of polygyny and The of this marriage system has been in large part to widespread (especially in which and and more affected arrangements and between and women's and and high are as additional factors and 1989; and 2009; and 2013). is no longer considered to be the normative context for and fertility. and childbearing marriage are in North Africa are levels and trends in age at first union to those in Southern Africa in this and Vallin 2013). a median age at marriage for women age has been in other continental sub-Saharan countries, there are of increases in age at first marriage in each region: and in Africa, in Eastern Africa, and most countries in the of in Western Africa For Eastern and Africa, the general picture is that of a slow but regular increase in age at marriage from the 1970s to the the increase has since with a median age of around years in most countries. to be the region with the most traditional of African nuptiality, Western Africa, however, does not from the general pattern of the postponement of women's first marriage is in all these countries, but with in timing and pace of can be distinguished in the of to with and long-term changes since the the Western to where the increase in age at marriage usually in the and the countries to with but trends. countries in Western Africa increased during the last In the 1960s and all countries in the region a pattern of early marriage while the of is larger years). The of cross-sectional indicators provides a first into the relationship between fertility and patterns of age at marriage. As for the recent there is a between them the higher the median age at first marriage in the the lower the As on the the is between Southern Africa marriage is especially late and fertility and the countries fertility remains over children per woman and women at earlier A result is that the at the country level was weak in the the late 1980s, than one of the was by the In other the in fertility levels during the period not with the in ages at marriage. However, at when the fertility transition in the 1990s with between countries, the becomes as if nuptiality when things to In this nuptiality to be the as its pattern more with the fertility level 10 years later than with the level of the same To further the relationship and temporal between changes in nuptiality and fertility, I will in two by examining nuptiality in the period of early fertility second, by the time to the years the transition. decline in most sub-Saharan countries taking an average of years for a 10 percent decrease in with regional means from years Africa) to more than 15 years Africa) (see Table The period when fertility decline is by changes in nuptiality, in the of early marriage patterns in the countries where it was still the of fertility transition (the historical of fertility to sustained a median age at first marriage below was still dominant in Eastern Africa percent of the and widespread in Western Africa percent of the At the onset of fertility decline TFR is 10 percent below the this pattern a minority in both percent in Eastern Africa, percent in Western Africa) and even common in and Southern Africa, where it was In most of sub-Saharan Africa percent of the the median age at marriage years at the onset of fertility transition. According to these an early marriage pattern with a sustained fertility on countries with consistent there is no empirical evidence of fertility transition in a context where the median age at marriage was below age at the time of the the and the onset of fertility decline, most countries percent of the sub-Saharan a postponement in women's age at first marriage. only in Africa, where the fertility transition later in a context in which age at marriage was to 20 In contrast, the in age at marriage was in Western Africa, where early marriage was the what is the picture before the fertility For countries, the time series on age at marriage at least years before the of fertility decline (i.e., it possible to examine the between nuptiality and fertility over a larger time for each of these countries, the in the

This chapter presents tables that represent the results of analysis of data on religion appearing in the World Religion Database . These data are collected from a number of sources including censuses, surveys, polls, religious communities, scholars, and others. After data collection and analysis, discrepancies are worked out and best estimates are made for each religion across a number of years. Results are presented for religionists and nonreligionists as a whole as well as for each religious and non-religious category. One of the tables lists the world's population in 1970, 2000, 2013, and 2030 with two separate 30-year growth rates (1970-2000 and 2000-2030) for comparison with data presented in the religion and nonreligion tables. Keywords: religion; World Religion Database; world's population

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).

Previous articleNext article FreeContributorsPDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreLESLEY BARTLETT ([email protected]) is a professor in educational policy studies and a faculty affiliate in anthropology. She does research in comparative and international education, literacy studies (including multilingual literacies), migration, and educator professional development. Her most recent book is Rethinking Case Study Research (Routledge, 2017).HELEN N. BOYLE ([email protected]) is an associate professor of international and comparative education in the College of Education at Florida State University (FSU), with a joint appointment in FSU’s Learning Systems Institute. Her research explores the evolving role that Islamic educational institutions, especially in North and West Africa and the Middle East, are playing in advancing national and international education goals. Her doctoral dissertation, funded through a Fulbright Dissertation Fellowship, is an ethnographic study of Moroccan Quranic preschools, for which she received the Gail P. Kelly Award for Outstanding Dissertation from the Comparative and International Education Society (2001). She has several journal articles and book chapters on Islamic education, as well as a book entitled Quranic Schools: Agents of Preservation and Change (Routledge, 2004). Her doctorate is in comparative and social analysis in education from the University of Pittsburgh, with a focus on international development education and a minor in anthropology.BARBARA BRUNS ([email protected]) is a Visiting Fellow at the Center for Global Development and adjunct instructor at Georgetown University, after a 30-year career at the World Bank as an education economist specializing in Latin America. She holds degrees from the University of Chicago and the London School of Economics.PABLO CEVALLOS ESTARELLAS ([email protected]) is the head of the UNESCO International Institute for Educational Planning’s Regional Office for Latin America, located in Buenos Aires, Argentina. A national of Ecuador, he holds master’s and doctorate degrees in education from Montclair State University (New Jersey, USA) where he was also a Fulbright/LASPAU Scholar.AMY JO DOWD ([email protected]) is senior director, education research, at Save the Children. She uses rigorous research to improve practice in international education and child development. Through innovations like SUPER, Literacy Boost, and IDELA, she supports program teams and policy makers to use data to optimize children’s learning.SARAH DRYDEN-PETERSON ([email protected]) is an associate professor at the Harvard Graduate School of Education. Her research and teaching focus on education in conflict and postconflict settings, particularly the role that education plays in building peaceful, participatory societies and enabling young people and their families to build envisioned futures in the midst of uncertainty.JEREMY D. JIMÉNEZ ([email protected]) is an assistant professor in the foundations and social advocacy department at State University of New York Cortland, where he primarily teaches courses in race/class/gender studies to future educators. He received his PhD in international and comparative education from Stanford University. His research interests focus on environmental justice and empathic discourse in social studies curriculum, and pedagogy.JULIA C. LERCH ([email protected]) is an assistant professor of sociology at the University of California, Irvine. Her research focuses on the sociology of education and comparative sociology. Recent publications appear in Gender and Society, Social Forces, International Sociology, Globalisation, Societies, and Education, and the European Journal of Education.ASSAF MESHULAM ([email protected]) is a lecturer in the department of education at Ben-Gurion University of the Negev. His research interests are critical education theory, education for democracy, and social justice and bilingual education. He is coauthor of the book The Struggle for Democracy in Education (Routledge, 2018).CELIA REDDICK ([email protected]) is a PhD candidate at the Harvard Graduate School of Education. Her research explores the intersection of education and migration, with a focus on educational policies and practices that support children’s and families’ well-being in settings of displacement. She is a former editor and co-chair of the Harvard Educational Review.BEN ROSS SCHNEIDER ([email protected]) is Ford International Professor of Political Science at Massachusetts Institute of Technology (MIT) and director of the MIT-Brazil program. He taught previously at Princeton University and Northwestern University. His recent books include Hierarchical Capitalism in Latin America: Business, Labor, and the Challenges of Equitable Development and Innovation in Brazil: Advancing Development in the 21st Century. Previous articleNext article DetailsFiguresReferencesCited by Comparative Education Review Volume 63, Number 2May 2019 Sponsored by the Comparative and International Education Society Article DOIhttps://doi.org/10.1086/702678 © 2019 by the Comparative and International Education Society. All rights reserved.PDF download Crossref reports no articles citing this article.

Cassava (Manihot esculenta) is a tropical crop that is grown in Africa, Latin America and Southeast Asia. Cassava was introduced from Latin America into West and East Africa at two independent events. In Uganda a serious threat to cassava's survival is the cassava mosaic disease (CMD). Uganda has had two notable CMD epidemics since the introduction of cassava in the 1850s causing severe losses. SSR markers were used to study the effect of CMD on the genetic diversity in five agroecologies in Uganda with high and low incidence of CMD. Surprisingly, high gene diversity was detected. Most of the diversity was found within populations, while the diversity was very small among agroecological zones and the high and low CMD incidence areas. The high genetic diversity suggests a mechanism by which diversity is maintained by the active involvement of the Ugandan farmer in continuously testing and adopting new genotypes that will serve their diverse needs. However, in spite of the high genetic diversity we found a loss of rare alleles in areas with high CMD incidence. To study the effect of the introgression history on the gene pool the genetic differentiation between East and West Africa was also studied. Genetic similarities were found between the varieties in Uganda and Tanzania in East Africa and Ghana in West Africa. Thus, there is no evidence for a differentiation of the cassava gene pool into a western and an eastern genetic lineage. However, a possible difference in the genetic constitution of the introduced cassava into East and West Africa may have been diminished by germplasm movement.

Institutional Quality and Income Distribution