Are We There Yet? Changing Migration Patterns in Rural Maine and Implications for Long-Term Population Growth

Analysis of the driving factors of U.S. domestic population mobility

The objective of this study is to provide empirical evidence on the relationship between rural and urban population on electricity consumption in five Sub-Saharan countries between 1971 and 2013. Results from the autoregressive distributed lag (ARDL)-bounds testing approach indicate that rural population plays a larger role in electricity consumption than the urban population in Côte d'Ivoire, Congo Republic and Zambia. In Congo Republic, a 1% growth in rural population resulted in a 29.4% decline in growth of electricity consumption in the long run. Growth in rural and urban population does not affect electricity use in Kenya and South Africa.

This paper builds a two-country, two-sector, non-scale growth model and investigates the relationship between trade patterns and the growth rate of per capita real consumption. We consider negative population growth as well as positive population growth. We show that, as long as the population growth rates of the two countries are different, if the country that accumulates capital stock has negative population growth, no trade patterns are sustainable in the long run. This is true irrespective of the population growth rate of the other country. Moreover, we show that, if the country that accumulates capital stock has positive population growth, two trade patterns are sustainable in the long run. In this case, either each country’s per capita growth is determined by the population growth of the capital-accumulating country or the population growth of both countries, depending on which of the two trade patterns is realized.

This paper uses the United Nations World Population Prospects 2022 data to screen out countries with endogenous negative population growth, i.e., aging population and fewer children, and then selects Italy, Japan, and Hungary as typical countries with negative population growth for analysis, combined with the availability of household consumption propensity data. Based on gray correlation analysis, this paper analyzes the relationship between consumption structure and economic growth in typical countries with negative population growth after entering the negative population growth time domain. The analysis results show that food consumption and health care have a greater pull on economic growth, and the correlation between housing consumption and economic growth, although weaker than the first two, still cannot be ignored. Combined with international experience, it can shed light on China, which is about to enter into negative population growth.

Attention to addressing undernourishment in low-and middle-income countries has expanded notably since the beginning of the 21st century. Population growth increases the overall demand for food, while income growth affects consumption patterns. Using annual aggregate data from the World Bank in 2001–2020 and econometric approaches, this research investigates the changes in the growth rates in rural and urban populations and GDP per capita and the prevalence of undernourishment as % of the population in low-income countries, lower-middle-income countries, and upper-middle income countries. The main goal of the study is to convey a deeper understanding by quantifying the impacts of rural and urban population growth as well as GDP per capita growth on the prevalence of undernourishment. The robust regression models showed that the prevalence of undernourishment in these countries was strongly associated with rural and urban population growth. A positive impact of rural population growth on undernutrition was found in all three groups of countries, with the most significant positive effects found in upper-middle-income countries. The negative effect of urban population growth on undernourishment was largest for the upper middle-income countries. Furthermore, fully modified ordinary least squares results revealed that the changes in the prevalence of undernourishment are mostly associated with long-term changes in the rural and urban population growth. The Difference in Difference (DiD) estimation confirmed only the causal effect of rural population growth on the prevalence of undernourishment in the panel of these countries. The findings of this study have both methodological and policy implications.

"This paper analyzes the effects of changes in fertility, mortality, and net migration patterns on the growth of school entry-age populations in three states (Florida, South Carolina, and West Virginia) over the period 1950-1990. Fertility changes have had the largest influence on growth of these young populations, as common sense suggests. Changing migration patterns have been quite important, however, in explaining intertemporal and interspatial variations in growth rates."

Bangladesh is the smallest nation in South Asia, with the eighth-largest and tenth-largest population density globally. The main goal of this article is to find the population growth and demographic transition (DT) in Bangladesh. The time series data used in this paper were gathered from population surveys, national censuses, and UN Population division, population projections and estimates. There are numerous variations in the population's size and age distribution due to Bangladesh's continuing demographic transition, which creates possibilities for the economy and society and difficulties for policy. It must be successfully controlled to develop this population into stronger and more stable economic and social development. Bangladesh's population and policymakers observe increasing population growth concerns, as the country's economy and policies heavily depend on both the population's size and growth rate. Bangladesh's changing demographics are creating new economic opportunities, and the findings presented in this paper could be useful to planners and policymakers to enhance Bangladesh's population policy. At the end of the pioneering study, we presented graphically what the population of Bangladesh could be in 2000- 2100 and the impact it would have on Bangladesh.

Analyzing the relationship between rural settlements and rural population change under different policy scenarios is key in the sustainable development of China’s urban and rural areas. We proposed a framework that comprised the mixed land use structure simulation (MCCA) model and the human–land coupling development model to assess the spatiotemporal dynamic changes in rural settlements and its’ coupling relationship with the rural population in the economically developed region of Deqing, Zhejiang Province. The results showed that rural settlements and urban land increased by 14.36 and 29.07 km2, respectively, over the last 20 years. The expansion of some rural settlements and urban land occurred at the cost of cropland occupation. Rural settlements showed an expansion trend from 2000 to 2020, increasing from 42.69 km2 in 2000 to 57.05 km2 in 2020. In 2035, under the natural development scenario, the cropland protection scenario, and the rural development scenario, rural settlements are projected to show an expansion trend and Wukang and Leidian are the key regions with rural settlement expansion. The distance to Hangzhou, nighttime light data, distance to rivers, and precipitation are important factors influencing the expansion of rural settlements. The coupling relationship between rural settlements and the rural population developed in a coordinated manner from 2000 to 2020. For 2035, under different scenarios, the coupling relationship between rural settlements and the rural population showed different trends. In the rural development scenario, the highest number of towns with coordinated development between rural settlements and the rural population is in Deqing, predominantly with Type I coupling. Overall, an important recommendation from this study is that the sustainable development of regional land use can be promoted by controlling the occupation of cropland for urban and rural construction, balancing rural settlement expansion and rural population growth, and formulating land use policies that are more suitable for rural development.

Introduction. Available data indicate that the effectiveness of the strategy of "90-90-90" varies considerably between countries. For example, Australia with figures 90-90-79 (2016) has not achieved the negative trend of the incidence and prevalence, while Niger 35-90-57 performance demonstrates a stable decrease in the HIV epidemic. One possible explanation for the observed processes may have different development of the epidemic process in populations that differ by demographic characteristics. From this we can assume that the epidemic control in a strategy of «90-90-90» or any other strategies will differ significantly in such self-regulating systems. Aim: modeling the dynamics of the epidemic process in populations with different probability of HIV transmission and negative, zero, positive population growth. Materials and methods : Computer probabilistic modeling by the Monte Carlo method was carried out. The following parameters were used to describe the epidemic process: population size, birth rate, mortality, HIV prevalence, lethality among patients with HIV/AIDS and probability of HIV transmission. The values of these parameters were close to the UNAIDS global statistics. It is assumed that the effective management of the epidemic reduce the probability of HIV transmission in the population. The dynamics of the population size, incidence and prevalence of HIV infection in populations with negative, zero, positive natural growth and the probability of HIV transmission in the population from 50% to 10% has been consistently studied. Statistical processing carried out by the Student method. Results and discussion. In populations with a negative population growth and a probability of HIV transmission of 0.5, incidence and prevalence at the initial stage are characterized by an increasing trend, reach peak values and decrease to zero. When reducing the probability of HIV transmission peak becomes plateau or directly take the downward shape. In general terms, similar patterns are recorded at zero population growth. The incidence and prevalence of HIV infection with a positive population growth are changing cyclically up and down. Change transmission probabilities range from 0.5 to 0.2 is characterized by a decrease in the frequency and amplitude of peaks increasing incidence and prevalence. When transmission probabilities at 0.1 epidemic process drops sharply. According to the simulation, any managerial impact in countries with negative population growth should be effective. Practical evidence does not contradict theory. For example, indicators "56-66-59" in Ukraine led to a decrease in the incidence. Management actions in populations with positive population growth that reduce the probability of HIV transmission by 20% or 40% are ineffective. When reaching 80%, the epidemic process abruptly stops. Conclusions. The effect of reducing the probability of HIV transmission in populations with a negative and zero population growth is expressed as a linear reduction in incidence and prevalence (at fixed lethality). In populations with a positive natural growth reduction transmission probabilities less than 40% strategically not effective, and when it reaches 80% potentiated abrupt cessation epidemic process.

China has the largest elderly population and is one of the fastest-growing economies in the world. Despite decades of economic growth, it is now facing a huge demographic challenge, i.e. rapid population ageing accompanied by negative population growth, which will have a long-term impact on the Chinese path to modernisation. Chinese modernisation is the modernisation of a large population, and of common prosperity for all. The well-being of the elderly population must be taken seriously. Since 2020, China has launched a proactive national strategy in response to population ageing to ensure that basic elderly care is accessible to the entire elderly population. This article analyses the characteristics of population ageing in China and the resultant challenges to social governance, social security, elderly services, liveable environments, and comprehensive development. It then introduces China’s distinctive and innovative responses to these problems, which manifest a joint governance model involving the active role of multiple stakeholders. Finally, it proposes some specific measures for addressing population ageing in the future to ensure both social stability and sustainable economic development.

"This study investigates the influence of school quality (measured at the high school level) on 1980 to 1990 population and employment change for nonmetropolitan fringe and hinterland census tracts in South Carolina. A Boarnet variation of the Carlino-Mills model is used to examine the interdependence of population and employment change.... Results...indicate that fringe tracts' population growth was positively related to student test scores, and hinterland tracts population and employment growth were negatively related to student-teacher ratios. Empirical results suggest that local school quality provided a positive influence on rural growth, primarily in terms of residential growth. The role of school quality for employment growth was less clear."

Part 1 Introduction: introduction and outline - demographic change and social expenditure, outline of the book. Part 2 Population ageing and migration: demographic transitions - the demographic transition theory, demographic transitions and economic growth economics and population ageing - population ageing and government budgets, population ageing and the labour force, other economic effects immigration issues - economic consequences of immigration other immigration issues. Part 3 Population decomposition: demographic characteristics - size and age structure of the population, determinants of population change socio-economic characteristics - factors affecting social expenditure, the adjustment process. Part 4 Population and expenditure projections: projection techniques - population projections, social expenditure, hypothetical examples population projections - benchmark population projections, projections with population decomposition Australian social expenditure - income support, provision of care and services social expenditure projections - the data, benchmark expenditure projections, population decomposition a stochastic approach - a deterministic model, a stochastic model, simulation results, the lognormal distribution.

Due to differences in definitions and measurement methods, cross-country comparisons of international migration patterns are difficult and confusing. Emigration numbers reported by sending countries tend to differ from the corresponding immigration numbers reported by receiving countries. In this paper, a methodology is presented to achieve harmonised estimates of migration flows benchmarked to a specific definition of duration. This methodology accounts for both differences in definitions and the effects of measurement error due to, for example, under reporting and sampling fluctuations. More specifically, the differences between the two sets of reported data are overcome by estimating a set of adjustment factors for each country’s immigration and emigration data. The adjusted data take into account any special cases where the origin–destination patterns do not match the overall patterns. The new method for harmonising migration flows that we present is based on earlier efforts by Poulain (European Journal of Population, 9(4): 353–381 1993, Working Paper 12, joint ECE-Eurostat Work Session on Migration Statistics, Geneva, Switzerland 1999) and is illustrated for movements between 19 European countries from 2002 to 2007. The results represent a reliable and consistent set of international migration flows that can be used for understanding recent changes in migration patterns, as inputs into population projections and for developing evidence-based migration policies.

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 objectives were to analyze the competitiveness of countries exporting fruit juices into Japan and simulate the effect of the negative Japanese population growth rate on fruit juice demand. The relative price version of the Rotterdam demand model was estimated for orange, grapefruit, other citrus, apple, pineapple and grape juices. Results indicate that most exporters can’t increase market share through price reductions. Product promotion and product differentiation is a more plausible option. The growth of fruit juice demand in Japan is expected to decrease over the period 2006 through 2020 for 11 of the 18 fruit juice/country combinations because of negative population growth rate.