Intra-Africa agricultural trade: A South African perspective

This paper analyses the development of agricultural foreign trade in Austria, Hungary and the Czech Republic with the aim of uncovering the changes that have impacted the Central European agricultural trade over the ten year period (1999–2008). It issues from the results of the analysis of agricultural trade in the aforementioned countries, which has changed dramatically in terms of the commodity structure, the territorial structure and primarily the value structure. The main changes to have caused most of the changes to the individual characteristics of agricultural foreign trade in the particular countries under analysis are the process of the EU enlargementy, the adoption of obligations to ensue from the EU membership and the concentration in the internal market of the EU countries. We can see the actual changes in the commodity and territorial structure of the trade carried out in the individual countries under analysis. The changes which have occurred resulted in a dominant share of the member countries of the EU 27 in the agricultural trade of the individual countries under analysis.

Purpose. As a European granary, Ukraine has rich agricultural resources. China is a country with a large population and has a large demand for food. However, the agricultural trade between the two countries has only achieved rapid development in recent years, and is not closely linked in related trade areas. This article studies the current situation of agricultural trade between the two sides, analyzes the trade data, finds out challenges, and provides suggestions for further promoting trade cooperation between the two sides.
 Methodology / approach. The categories 1, 2, 3, and 4 under the HS Code of the General Merchandise Trade Statistics Database of the People’s Republic of China are used as agricultural trade statistics categories with a time span of 2014–2019 trade data. The relevant data of agricultural product trade are sorted and there is statistics, which objectively explain the current status of trade exchanges between the two sides; the results of the analysis of the trade data are put forward, and the problems existing in the development of China-Ukraine trade and the factors affecting the development are raised.
 Results. According to the results of the analysis, from 2014 to 2019, China’s agricultural products imported from Ukraine accounted for 45.5 % of the total import trade. Agricultural products occupy a very important position in China’s import trade from Ukraine in terms of trade scale. Among them, the import of cereals and other products accounted for 95.7 % of the total imported plant products. Ukraine, the European granary, has become China's main food importer. In 2019, China replaced India as the largest food importer of Ukraine; In terms of export trade, mechanical and electrical products, base metals and light industrial products occupy an important position. From 2014 to 2019, China’s agricultural exports to Ukraine accounted for only 2.6 % of the total export trade, which is a small share. This shows that China and Ukraine have strong complementarity in terms of total trade volume and agricultural product trade. In the future, the two countries can further strengthen cooperation to expand their own advantages and better promote the development of trade between the two countries.
 Originality / scientific novelty. Due to the geographical distance between China and Ukraine, the political instability of the post-independence republic as a former Soviet Union led to less economic and trade exchanges between Ukraine and China. Scientists did not study much on economic and trade relations between China and Ukraine. Most of the research deal with macro trade policy aspects, but the innovation of this article lies in the use of statistical data for empirical analysis, to show the current status of trade between the two sides, and to make recommendations for the further development of bilateral trade.
 Practical value / implications. Since the establishment of diplomatic relations between China and Ukraine in 1992, the relations between the two countries have developed smoothly. In 2011, the two countries established a strategic partnership of cooperation. Subsequently, trade cooperation in various fields has continued to develop, especially in the agricultural product trade. In recent years, total agricultural trade has doubled. In 2019, China became the largest importer of Ukrainian agricultural products. After China proposed the implementation of the «Belt and Road» initiative, Ukraine actively participated in the «Belt and Road» framework agreement. The research on agricultural trade between the two sides will be of great significance to promote further and deeper cooperation between the two sides in the field of agricultural trade and expand and create a wider development space.

Agricultural trade policies in the new millennium

It is widely believed that the countries of Africa trade relatively little with the outside world, and among themselves, despite an extensive network of regional trade agreements. We examine this proposition by focusing on agricultural trade. Specifically, we ask whether non-tariff barriers (NTBs) are stunting agricultural trade within the Economic Community of West African States (ECOWAS), a grouping of fifteen countries in West Africa that has removed tariffs on agricultural trade among its members. Our survey of truckers in Tambacounda (Senegal) in August 2009 found evidence of extensive bribery by police and border officials, effectively representing a barrier to trading. We estimate a unit-elastic structural gravity model of agricultural trade, using data from 135 countries for 2000, 2003 and 2006, and employing Tobit and other types of structural specification. A robust result emerges: agricultural trade among the countries of ECOWAS is higher than one would expect. This does not mean that there are no NTBs within ECOWAS, but it does imply that any such barriers are less harmful to agricultural trade in ECOWAS than in the world as a whole. Similar effects are found for the Common Market for Eastern and Southern Africa (COMESA) and the South African Development Community (SADC). This suggests that African countries are not averse to agricultural trade, and local traders have been effective at exploiting trade opportunities. Copyright 2013 , Oxford University Press.

Along with the strong propositions of freer bilateral or multilateral trade advocated by numerous trade organizations, traditional customs tariffs have been reducing or even cutting down in recent years. However, more and more non-tariff barriers to trade (NTBs) are prevailing and attracting attentions from policy makers and academic researchers nowadays. As a matter of fact, there are large amounts of policy measures can be regarded as NTBs which refer to all the measures that can distort the trade excluding tariffs. This dissertation attempts to study the influence of NTBs and quantitatively measure the effects of NTBs on trade flows and welfares based on the analyses of some trade policies and empirical trade data. As mentioned already, there are actually many categories of NTBs, and in the process of selecting objects as the study issues, two principles are followed. One of the principles is that those NTBs to be discussed in this dissertation should be hot and controversial topics in recent years and thus are worthy to do research. Moreover, the other principle is that those NTBs to be analyzed should have the impacts on agricultural product trade and distort the agricultural trade flows. Therefore, three kinds of NTBs—foreign exchange rate control, positive list system, and import quota are chosen to be studied in this dissertation. In fact, the models constructed in this research can be applied to analyze many kinds of NTBs, and hereinto the above three policy cases are taken into consideration and empirically examined their effects on agricultural trade. The first study focused on China’s exchange rate reform. In order to protect local industries and weaken the exporting advantages of China, many countries have begun to put much pressure on the appreciation of Chinese currency. In the context of huge pressures from many countries, in July 2005, China decided that Chinese currency yuan would no longer only be pegged to U.S. dollar but move into a managed floating exchange rate regime with reference to a basket of currencies. Although exchange rate had been paid much attention by economists since long time ago, yet nowadays this topic is still popular and moreover, now the study subjects become focusing on exchange rate volatility instead of exchange rate itself. Therefore,

Identifying growth poles in the SSA region, strengthening linkages and generating mutual benefits across African countries is an important part of the strategy to promote agriculture-led growth at the Africa-wide scale. Using agricultural trade data, this study focuses on identifying major countries that play important roles in regional agricultural trade and commodities in which African countries have a comparative advantage and where there is potential for more trade within the region....Poor infrastructure and institutional barriers are among the major reasons constraining African countries to exploit their comparative advantage and strengthen their economic linkages. The model simulations show that opening the EU market is strongly in the common interest of African countries. Reducing African countries own trade barriers, both in agriculture and non-agriculture, can significantly increase intra-regional agricultural trade. However, the benefit of the globalization and agricultural trade liberalization to the African countries would be limited by poor market access conditions such as transportation and other infrastructure. Because of these, many African agricultural commodities can hardly reach domestic and regional markets, or be exported to the world. Without improving the efficiency of these nonagricultural sectors that provide critical inputs or services to agricultural production and trade, it is virtually impossible for the countries of SSA to increase their competitiveness in international markets. from Authors' Abstract

This article is a first attempt to understand the importance of the different export destinations and export commodities for South African agriculture. This knowledge of agricultural trade movements will now make it possible to reprioritize efforts in an agricultural export promotion strategy. The article uses a technique called the ‘growth-share’ matrix which maps countries according to their significance in South Africa's current agricultural export picture (share) and the rate at which they are becoming increasingly more or less significant (growth) in South Africa's agricultural trade. Analysis on agricultural export share and growth shows that traditional export products and countries are still important, though steadily declining. Major ships have taken place over the last decade, but especially after 1993. High growth export destinations has grown from a 31/2 % share to almost 30% share (1992 to 1996). High growth export products has grown from 31/2 % share to 33% share (1988 to 1996). The EU has systematically declined in importance as export destination. Africa has increased dramatically during the sanction years, but flattened after 1993, but it maintained a high base. South Africa's greatest opportunities for export growth are Asia and the Americas.

PurposeThe purpose of this study is to explore the effect and mechanism of the digital economy’s influence on the binary margin of agricultural exports.Design/methodology/approachBased on the theoretical analysis of the mechanism of the digital economy’s influence on the binary margin of agricultural exports, this study empirically examines the effect and mechanism of the digital economy’s influence on the binary margin of agricultural exports based on China’s customs export data from 2011 to 2016.FindingsThe relevant findings are threefold. (1) The digital economy significantly improves the binary margin of agricultural exports, and its effect on the intensive margin is stronger than that on the expansive margin. After the expansive margin is subdivided, the effects on the three sub-variables of the expansive margin are in the following order: old products exported to new markets > new products exported to old markets > new products exported to new markets. (2) The heterogeneity analysis reveals that the digital economy has a stronger role in promoting the binary margin of exports for enterprises in the eastern region, high-income countries as the destination of exports and state-owned enterprises. (3) Mechanism analysis shows that the digital economy promotes the binary margin of agricultural exports by reducing trade costs and intensifying market competition.Originality/valueFirst, in terms of research perspective, although there are some studies on the impact of the digital economy on export trade in existing literature, the research objects mainly focus on manufacturing enterprises. In fact, agricultural trade is susceptible to natural conditions and seasonal factors, and countries may impose more SPS measures and TBT measures on agricultural trade due to risk considerations. The relationship between the digital economy and agricultural trade also has its own characteristics, but there are few research studies in this area. At present, only Liu and Gao (2022), based on the data of total imports and exports of different agricultural products from 2004 to 2018, have established a vector auto-regressive model to empirically analyse the heterogeneous dynamic impact of the digital economy on the trade volume of agricultural products. In addition, Ma and Guo (2023) conducted an empirical test on the total effect, regional heterogeneity and threshold effect of the digital economy on agricultural export trade based on China’s provincial panel data from 2011 to 2020. Therefore, under the new circumstances of continuous integration of digital technology and agriculture, this study interprets the impact effect and mechanism of the digital economy on the binary margin of agricultural exports from the perspective of the digital economy, providing new research perspectives and approaches for promoting the growth of agricultural exports. Second, in terms of theoretical analysis, the above studies have not been fully analysed in terms of the specific mechanism of the impact of the digital economy on agricultural exports. Based on the positive and negative characteristics of agricultural trade, this study introduces two kinds of roles into the theoretical analysis framework to comprehensively determine the trade impact effect of the digital economy. Third, in terms of research design, this study empirically examines the impact of the digital economy on the binary margin of agricultural products, passing a series of robustness tests and investigating the mediating roles of trade cost and market competition effects, producing an empirical basis for China to leverage the digital economy to promote the binary margin of agricultural exports.

PurposeThe outbreak of the novel COVID-19 virus has spread throughout the world, causing unprecedented disruption to not only China's agricultural trade but also the world's agricultural trade at large. This paper attempts to provide a preliminary analysis of the impact of the COVID-19 pandemic on China's agricultural importing and exporting from both short- and long-term perspectives.Design/methodology/approachThis study seeks to analyze how the outbreak of COVID-19 could potentially impact China's agricultural trade. With respect to exports, the authors have pinpointed major disruptive factors arising from the pandemic which have affected China's agricultural exports in both the short and long term; in doing so, we employ scenario analysis which simulates potential long-term effects. With regard to imports, possible impacts of the pandemic regarding the prospects of food availability in the world market are investigated. Using scenario analysis, the authors estimate the potential change in China's food market—especially meat import growth—in light of the implementation of the newly signed Sino-US Economic and Trade Agreement (SUETA).FindingsThe results show that China's agricultural exports have been negatively impacted in the short-term, mostly due to the disruption of the supply chain. In the long term, dampened external demand and potential imposition of non-tariff trade barriers (NTBs) will exert more profound and lasting negative effects on China's agricultural export trade. On the other hand, despite panic buying and embargoing policies from some exporting and importing countries, the world food availability and China's food import demand are still optimistic. The simulation results indicate that China's import of pork products, in light of COVID-19 and the implementation of SUETA, would most likely see a sizable climb in quantity, but a lesser climb in terms of value.Originality/valueAgricultural trade in China has been a focal-point of attention in recent years, with new challenges slowing exports and increasing dependence on imports for food security. The outbreak of the COVID-19 pandemic adds significant uncertainty to agricultural trade, giving rise to serious concerns regarding its potential impact. By exploring the impact of the unprecedented pandemic on China's agricultural trade, this study should contribute to a better understanding of the still-evolving pandemic and shed light on pertinent policy implications.

Purpose The global trading environment has experienced a significant transformation with the establishment of the World Trade Organization (WTO) and the growing prevalence of Regional Trade Agreements (RTAs). In line with this global trend, China, which joined the WTO in 2001, had signed 16 RTAs with 24 countries and regions by 2018. Furthermore, in 2013, the Chinese government launched the Belt and Road Initiative (BRI), aimed at fostering regional integration and economic growth through extensive infrastructure projects and trade liberalization agreements (Huang, 2016; Zhao et al., 2024). Despite growing global integration, trade continues to be shaped by border effects – the restrictive impact of national and geographic boundaries on trade flows, particularly in the sensitive agricultural sector. To explore this issue, this study analyzes how border effects shape China’s agricultural trade and examines the impact of RTAs and the BRI on trade dynamics. Design/methodology/approach This study employs a gravity model to examine the impact of border effects on agricultural trade between China and its 36 trading partners, including Brazil, Japan, South Korea, the European Union and the United States, over the period from 2001 to 2018. Findings The results indicate that China’s border effects on agricultural imports and exports are asymmetric, with import border effects being larger than export border effects. From 2002 to 2018, these border effects have generally declined, reflecting reduced trade frictions and deeper integration into the global market. Additionally, country-specific border effects reveal that the greater a country’s reliance on agricultural imports from its trading partner, the lower its border barriers tend to be. While RTAs and the BRI have positively influenced trade, the persistently high border effects observed in some participating countries suggest that factors beyond trade agreements play a crucial role in shaping trade barriers. Originality/value Asymmetric border effects underscore the need for a country-specific policy approach that effectively addresses these disparities. To further reduce border effects and foster trade, efforts should focus not only on lowering tariff and non-tariff barriers through policy factors but also on addressing non-policy factors such as home bias, which can hinder cross-border trade expansion.

Decades of government intervention have helped develop the South African agriculture sector to its present state. Policy reforms have included trade and exchange rate policies to increase the country's international competitiveness, reduce poverty and promote economic growth. These reforms are facilitating the growth in agricultural trade and South Africa's reintegration into the global economy. Annual agricultural exports and imports have increased. This paper uses annual data and a vector error-correction model to investigate the supply and demand relationships for agricultural trade flows in South Africa during the past four decades. The results show that prices, real exchange rates, domestic production capacity and real incomes have significant impacts on the country's agricultural trade. In particular, exchange rate volatility has negative impacts. This cannot be viewed solely as an exogenous source of macroeconomic instability in South Africa, as domestic policies play a crucial role in influencing the movement of exchange rates.

Purpose In response to trade disputes with the USA and Canada, while the United States and Canada have weaponized tariffs as a trade policy tool, China has weaponized food as a foreign policy tool by targeting agricultural imports from these countries. This paper highlights interdependencies in food security, market access, and diplomacy, offering a comparison between Sino-US and Sino-Canada relations in contemporary times. Design/methodology/approach This paper draws on trade data as well as academic literature, government reports, and policy documents to contextualize historic trade dynamics and trace the buildup to recent disputes. Using a comparative framework, we analyze how Sino-US and Sino-Canada relations have shaped, and continue to shape, agri-food trade flows. Findings Our analysis reveals structural vulnerabilities in both US and Canadian agricultural exports to China, emphasizing how reliance on a narrow set of commodities leaves both countries exposed to economic losses resulting from China’s trade policies. Simultaneously, China’s dependence on a limited number of suppliers for large volumes of key commodities makes it vulnerable to price volatility and supply uncertainty. Research limitations/implications As China’s global economic footprint grows, so does its capacity and willingness to shape trade relationships in ways that align with its broader geopolitical and domestic policy objectives. This paper has analyzed the trade dynamics between China and two of its most important agri-food partners—the United States and Canada—through historical, comparative, and policy lenses and draws insights that may inform and shape future policy directions for respective countries with significant implications for international trade and the world economy. Social implications The evolution of China’s agricultural trade relations with the United States and Canada underscores a profound shift in the global political economy—one in which interdependence can no longer be taken as a guarantee of stability, and where strategic considerations increasingly influence the trade flows. The comparative analysis of China-US and China-Canada agricultural trade relations highlights the complex interplay between economic interdependence, geopolitical tensions, and strategic vulnerabilities and carries significant social, economic, and political implications. Originality/value Building on prior research that has identified sensitivities in Sino-US and Sino-Canada agricultural trade, this paper offers a novel perspective in a comparative analysis of how the Sino-US and Sino-Canada agricultural trades evolve over time and how China manages its agricultural trade with both countries, especially under the current Trump tariff shocks.

As Thailand's most important trading country and partner, China has become Thailand's most important exporter of agricultural products. In the context of the "Belt and Road", such agricultural trade is another good opportunity. This paper uses the data of agricultural trade between China and Thailand in recent years as a reference to analyze the characteristics of agricultural trade between China and Thailand and the scale and structure of bilateral trade, and analyzes the countermeasures for the development of agricultural trade between China and Thailand in view of the challenges of the development of bilateral agricultural trade between China and Thailand under the promotion of the Belt and Road Strategy. This paper comprehensively summarizes the competitiveness and complementarity of agricultural trade between Thailand and China under the background of the "Belt and Road", so as to further strengthen bilateral exchanges and cooperation and promote agricultural trade between the two countries to a higher level. At present, the trade of agricultural products between China and Thailand was facing a challenge. The scale of Sino-Thai agricultural trade has been reduced, the degree of compatibility has weakened, the category is clustered, and the non-tariff trade barriers rely on the depth of Sino-Thai agricultural products. This article analyzes the situation of agricultural trade between China and Thailand to discuss the current shortcomings of China and Thailand, and where China and Thailand can seek cooperation, because of the win-win cooperation, drive the economy, and promote the depth of agricultural trade in agricultural products.

Previous studies on the J-curve hypothesis for South Africa have relied on aggregate trade data between South Africa and the rest of the world, or on similar data for trade between South Africa and its major trading partners. The evidence of J-curve effects in South Africa's bilateral trade have been mixed. In this paper, we revisit this issue by examining the short- and long-run effects of exchange-rate changes on trade flows using disaggregated industry data on bilateral trade between South Africa and the United States. From estimates of trade balance models using the autoregressive distributed lag (ARDL) approach, we find evidence of significant J-curve effects, as a depreciation of the South African currency has favourable short-run effects on trade balances for eight industries. These short-run effects continue into the long run for a quarter of the industries considered in the study. The results also show that income has significant long-run effects on trade flows in industries that account for almost 55% of trade flows between South Africa and the United States.

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