Environmental dimensions of managing public budgets and public enterprises in Sub-Saharan Africa

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

Globally, financial inclusion is regarded as being crucial for balancing an economy’s financial system. However, despite the significance of financial inclusion, it still needs to be clarified to identify what factors are responsible for the diverse trend of financial inclusion in the forty-five Sub-Saharan Africa (SSA) countries from 1999 to 2024. The main rationale of the study empirically investigated these determinants of financial inclusion in forty-five Sub-Saharan Africa (SSA) countries from 1999 to 2024, which covers three distinct periods: which is the pre-COVID, 2020–2022 is the COVID period, and the post-COVID period from 2023 onward, but examined as a whole from 1999 to 2024 for easy policy formulation for SSA countries. The study was anchored on two main research objectives: firstly, to examine the factors influencing financial inclusion in Sub-Saharan Africa (SSA) in these three distinct periods, and lastly, to present the policy implications of the result of these factors in enhancing financial inclusion in the post-COVID era in SSA. The study used the Panel Least Squares (PLS) technique in the data analysis. The result revealed that economic growth (GRO), Islamic banking (ISMAIC), money supply (MSS), internet users (USERS), and credit availability (CREDIT) positively and significantly enhance financial inclusion with coefficients of 0.001298, 4.926809, 1.08 × 10−6, 0.459388, and 0.657431, respectively, with significant p-values of 0.0008, 0.0023, 0.0000, 0.0000, and 0.000, respectively. On the flip side, internet servers (SERVER) have a negative coefficient value of 4.63 × 10−6 with a p-value of 0.000. Though inflation (INFL) and interest rate (INT.) have negative coefficient values of −0.02853 and −0.08317, they have insignificant p-value impacts of 0.2841 and 0.2501, respectively. The result indicates that many of the variables have a significant impact on financial inclusion. This is shown from the probabilities of the t statistics of each of the independent variables in the estimated model, which are significant at the 5% level. The policy implications of these results include the following: firstly, SSA governments should promote economic growth through investment in productive sectors, infrastructure development, and job creation programs to indirectly improve financial inclusion. Secondly, SSA countries’ policymakers should maintain price stability through sound monetary and fiscal policies to ensure inflation does not hinder access to financial services. Thirdly, SSA countries’ governments and central banks should promote lower interest rates and enhance credit accessibility, especially for marginalized groups, through subsidized loans and targeted credit schemes. Fourthly, policymakers should support the expansion of Islamic finance by improving regulatory frameworks and increasing awareness about Sharia-compliant financial products.

Intra-cerebral Haemorrhage (ICH) seems more prevalent in sub-Saharan Africa (SSA) than in High-Income Countries (HIC) with poorer clinical outcome. Higher impact of hypertension and/or amyloid angiopathy could account for this disproportion. Here, we sought to (i) retrospectively compare ICH clinical and imaging patterns in Belgium and Guinea and in a subsequent cohort (ii) prospectively compare brain MRI characteristics to seek evidence for a different proportion of amyloid angiopathy patterns. Ninety one consecutive patients admitted for spontaneous ICH at Brussels Erasme-ULB Hospital and at Conakry Ignace Deen-UGANC were retrospectively compared in terms of ICH volume estimated with the ABC/2 method, clinical characteristics and modified ranking (mRS) score at 30days. mRS was dichotomised as good outcomes (≤3) and poor outcomes (>3). A prospective cohort of 30 consecutive patients with ICH admitted at CHU Conakry Ignace Deen-UGANC was prospectively included to undergo brain MRI. Results of the Guinean MRI were compared to 30 patients randomly selected from Brussels' initial cohort. Paired Student's t-test and Mann-Whitney u-test were used for group comparisons. Age of ICH onset was higher in Belgium (68±17years vs. 56±14years, P<0.01) while ICH volume and 30-day mortality rate were higher in Guinea (20ml vs. 11ml, P<0.01 and mortality 33% vs. 10 %, P<0.01). ICH burden in survivors in Conakry and Brussels showed respectively good outcomes in 56.7% and 60.4% (P=0.09) and poor outcomes in 10.3% vs. 29.6% (P<0.001). MRI analysis of the prospective cohort failed to disclose significant differences regarding brain MRI characteristics. Intra-cerebral Haemorrhage affected patients 15years younger in Guinea with larger haematoma volumes and higher mortality than in Belgium. MRI findings did not show more prevalent amyloid angiopathy pathology suggesting that better primary prevention of hypertension could positively impact ICH epidemiology in Guinea.

The results of a comparative study of the effects of state and public enterprise management in transition economies have classified Serbia as a group of countries with inefficient management. Transition countries, members of the EU, achieve better management performance of state-owned enterprises than non-EU countries, due to stronger institutions, efficient state aid rules, as well as greater transparency of budget support. The global pandemic in 2020 and increased health, economic and social risks, in many transition countries have not affected the slowdown of structural reforms in public enterprises, on the contrary, a special focus was directed towards upgrading the mechanisms of management of public enterprises, strengthening the anti-corruption policy, digitalization of state services, etc. The research of the reform balance of the management of large republican public infrastructure enterprises in the Republic of Serbia has shown that the state has major problems in the management of enterprises that are in its portfolio and under its control. Their business poses a permanent budget risk, directly affecting economic growth and key macroeconomic aggregates. Large infrastructure public enterprises have a dominant share in the energy and transport sectors, which affects the business of the entire economy. The balance of their business in the period of economic boom is in increasing discrepancy with the balance of business of the private sector, which clearly indicates the problem of management. The primary reform task in the management of public enterprises is to improve the accountability, transparency and efficiency of their management.

The unprecedented outbreak of coronavirus disease (COVID-19) drastically spread worldwide, resulting in extraordinary measures put in place in various countries including Sub Saharan Africa (SSA) countries. To assess the factors associated with compliance with the public health measures imposed by various SSA countries. Cross sectional study using self-administered surveys distributed on social media platforms between April 18th and May 16th, 2020, corresponding with the mandatory lockdown period in most SSA countries. Multivariate analysis examined the associated factors. The prevalence of hand hygiene, quarantine, self isolation practices, wearing of face mask and attending large gatherings during COVID-19 were 94%, 39%, 31%, 64% and 14%, respectively. In multivariate models, older age 49+ years: adjusted OR 2.13, 95%CI 1.22,3.71), females (OR 1.41,95%CI 1.03,1.93), Central African countries (OR 3.73,95%CI 2.02,6.87) were associated with wearing face mask. Living alone (aOR 1.52,95%CI 1.04,2.24) during the lockdown was associated with avoiding large gatherings including religious events. Female respondents (aOR 1.61, 95%CI 1.30, 2.00), married (aOR 1.71,95%CI 1.33,2.21) and unemployed (aOR 1.62,95%CI 1.25,2.09) SSAs were more likely to practice self-quarantine measures. The low prevalence of mitigation practices suggest the need for targeted education campaign programs to sensitise the population.

The consequences of carbon dioxide (CO2) emissions in Sub Saharan Africa (SSA) countries cannot be ignore given it adverse effect on human health and global warming. With rising CO2 emissions and fallen volume of trade openness and FDI inflows in recent time, we seek to examine the effect of trade openness and foreign direct investment (FDI) on environmental degradation using time series data from 1975 to 2020 in SSA. Using the environmental Kuznets curve (EKC) framework, the study employs a quadratic modeling and turning point approach to realize the study objectives. The findings reveals that (1) the trade openness-EKC and FDI-EKC does not hold given the presence of decreasing effects in the short run and increasing effects in the long run; (2) it confirms that a U-shaped trade openness-emissions and FDI-emission nexus holds given the decrease in trade openness and FDI in the short run and an increase in trade openness and FDI in the longrun; (3) The analysis supports the halo effect hypothesis before the turning point but the pollution haven hypothesis sets in after the turning point; (4) it shows evidence that trade openness and FDI contributes to reduce CO2 emissions in the short but increase it in the long run. The study recommends that SSA countries should adopt stringent environmental policies to attain sustainable economic growth without associative harm to the environment.

In Indonesia, the shift from the Old Order to the New Order Government in 1966 and there after was characterized by economic reforms, including reform of public enterprises. The first major reform in the area of public sector enterprises was restructuring a single form of public enterprises (Perusahaan Negara or PN) into three forms (Perjan, Perum and Per sew). The second round of reform occurred recently in public enterprise restructuring and simplification of the system of control. Both the first and the second reforms were concerned with problems of inefficiency, with debureaucratization and decontrol being the remedies. In examining these approaches, this article argues that the type of relationship between government (the principal) and public enterprises (the agents) is more than likely the central difficulty of public enterprises in Indonesia. Therefore, improvement in public enterprise management and performance probably depends on improving the relationship between principal and agent.

This study was carried out to investigate the influence of colonial culture on the banking systems of countries in Sub Saharan Africa. The objectives were to find out the elements of colonial culture inherited by countries in Sub Saharan Africa, to assess how colonial culture influenced the banking systems of French speaking and English speaking countries in Sub Saharan Africa and the points of convergence and divergence between the culture and banking systems of French and British colonies in Sub Saharan Africa. This study adopted the qualitative research strategy and content analysis. The results of the study showed that the three main elements of colonial culture inherited by countries in Sub Saharan Africa are language, religion and the legal system. The African countries were converted from their local languages to English and French languages, from African traditional religion to Christianity and adopted the civil and common law from France and Britain. All these elements of colonial culture influenced the banking systems of the countries. Points of convergence were that both countries used hierarchical administrative structure for their colonies and banking institutions, focused on economic philosophy during colonialism and after WW II introduced reforms to better administer their territories and business entities in Africa, including their banking institutions. However, the points of divergence were integration versus assimilation, direct versus indirect rule, civil versus common law, as well as forced labour versus recruitment. The study concluded that colonial culture has an influence on the banking systems of countries in Sub Saharan Africa.

Globally, financial inclusion is regarded as being crucial for balancing an economy's financial system. However, despite the significance of financial inclusion, it still needs to be clarified to what extent it is practiced. In this study, the assessment of the performance of the 49 Sub Saharan Africa (SSA) countries as regards financial inclusion was carried out through the construction of a single composite Financial Inclusion Index (FII) to capture the four dimensions of financial inclusion. Z-sum score and Principal Component Analysis (PCA) was deployed to analyse the secondary data spanning from 1999 to 2023. The decision rule deployed was that if a value for Financial Inclusion Index (FII) from zero to forty-nine (0-49) this shows that there is low financial inclusion; medium financial inclusion from fifty to sixty-nine (50-69) while, a value from seventy and above (70- above) denotes the high financial inclusion) for the study period. The result revealed that all SSA countries are low that is they fall into between zero (0) to forty-nine (49) in the composite financial inclusion index in line with the decision rule. Also, the result indicated that Sao-Tome and Principe was the best performing SSA country with the highest financial inclusion penetration per thousand considered during the period of the study.

In 2004 the United Nations University World Institute for Development Economics Research (UNU-WIDER) embarked on a large-scale research project on the ‘Impact of Globalization on the World’s Poor’ co-directed by Machiko Nissanke and Erik Thorbecke. The first conference was essentially conceptual in nature, meant to understand better the various mechanisms and channels through which globalization affects the poor either directly or indirectly. The other three conferences focused on each of the major regions of the developing world: Asia, Africa and Latin America. Theobjectivesofthisintroductionarethreefold:first,toreviewbrieflyhow the forces of globalization influence poverty in general; second, to describe and discuss the main transmission channels and mechanisms; and third to analyze the impact of globalization on Africa and present an overview of the six Africa case studies included in this issue. 1. The Impact of Globalization on the World’s Poor Globalization provides a strong potential for a major reduction in poverty in the developing world because it creates an environment conducive to fastereconomicgrowthandtransmissionofknowledge. 1 However,structural factors and policies within the world economy and national economies have impeded the full transmission of the benefits of the various channels of globalization for poverty reduction. In particular sub-Saharan Africa (SSA) hasbeenrelativelylessaffectedbytheforcesofglobalizationthanotherparts of the world. World income distribution continues to be very unequal and many poor countries particularly in Africa are stagnating. Moreover, there is much empirical evidence that openness contributes to more within-country

Infectious disease is a serious global health problem. However, its negative impact is felt more in sub Saharan Africa. Epidemiological figures have shown that the burden of infectious disease is highest in the African continent especially in sub Saharan Africa. A majority of people leaving in sub Saharan Africa are poor and their exposure to infectious disease worsens their poverty situation. Aside impoverishing infected individuals, infectious diseases also directly negatively impact the economy of the country involved, reducing economic growth and productivity. Diseases like malaria, HIV, tuberculosis TB, and other epidemic prone diseases like Cholera and Ebola constantly pose a threat to economic development in Sub Saharan Africa. It is therefore pertinent to invest in disease prevention strategies to curb the burden and reduce the impact of infectious diseases on the economy of countries in sub Saharan Africa. This mini review re-echoes the burden of infectious diseases and discusses ways and strategies to reduce the burden of these diseases and the economic benefits that will be accrued at the long run.

Appraising the spillover effect of water use efficiency indicators in sub- Saharan Africa: A spatial econometric approach

Purpose - The purpose of this study is to investigate the impact of remittances on economic growth of least developed countries in Sub Saharan Africa (SSA). Design/methodology/approach - This study made use of annual data for the period of 1974 to 2017. It employed a time series model referred to as Autoregressive Distributed Lag (ARDL) model. Findings - Empirical results depicts that there exists a statistically significant long run negative relationship between remittance and economic growth in Benin, Burkina Faso, Lesotho and Togo. But it was only in Senegal that depicted a positive and statistically significance relationship between remittance and growth. Research implications or Originality – Previous work which studied remittances in SSA based on panel data which is mostly generalized. The study in each individual country has never been done before for these least developed countries, therefore, the impact of individual countries has never been done before. Consequently, our work contribute to the existing literature in that, we examine remittances on least developed countries(LDCs) individually, in addition we observe the impact in both short run and long run by using ARDL model.

PurposeThis paper aims to investigate the long-run impact of selected foreign capital inflows, including aid, remittances, foreign direct investment (FDI), trade and debt, on the economic growth of 21 low-income countries in the Sub Saharan Africa (SSA) region, during the period 1990–2018.Design/methodology/approachTo obtain this objective and for robust analysis, a parametric approach, which was dynamic ordinary least squares, and a non-parametric technique, which was fully modified ordinary least squares, were used.FindingsThe results of both models confirmed that, in the long run, trade and aid affected the growth rate of the per capita income in these countries in a positive way. However, external debt seemed to have an adverse influence on such growth.Originality/valueFirst, this is the initial study that has addressed this matter across a homogenous group of countries in the SSA region. Second, while most of the previous studies regarding capital inflows into the SSA region have focused on the impact of only one or two aspects of such foreign capital inflows on growth, the present study, instead, examined the impact of five types of foreign capital inflows (aid, remittances, FDI, trade and debt).

As the importance of quality in health care provision is increasingly recognised, it is opportune to consider quality care as a key link between clinical and public health approaches to human immunodeficiency virus (HIV) infection in developing countries, especially in sub-Saharan Africa. This region has the lion's share of the global epidemic and the least resources to respond. Looking at health problems using a 'quality lens' may help bridge the gaps between clinical care and public health, the current and desired standard of care, and prevention and treatment. Quality care, with prompt diagnosis and effective treatment, of people with HIV infection is crucial for good individual health outcomes, public health outcomes (in terms of decreased HIV transmission) and societal outcomes (increased productivity and decreased costs of health provision for HIV-related care). A spotlight on quality care can bring clinicians and public health practitioners together in working towards universal access to quality HIV care and prevention - one of the greatest health challenges faced in developing countries in Africa today.