East Africa

This page provides an overview of the key characteristics of East Africa along its likely (or Current Path) development trajectory. The Current Path forecast from the International Futures forecasting (IFs) platform is a dynamic scenario that imitates the continuation of current policies and environmental conditions. The Current Path is therefore in congruence with historical patterns and produces a series of dynamic forecasts endogenised in relationships across crucial global systems. We use 2019 as a standard reference year and the forecasts generally extend to 2043 to coincide with the end of the third ten-year implementation plan of the African Union’s Agenda 2063 long-term development vision.

East Africa is the eastern subregion of the African continent. According to the African Futures classification, 12 territories make up East Africa/the Horn of Africa: Burundi, Comoros, Djibouti, Eritrea, Ethiopia, Kenya, Rwanda, Somalia, Sudan, South Sudan, Tanzania and Uganda.

Tanzania, Kenya, Uganda, Rwanda, Burundi and South Sudan are members of the East African Community. Djibouti, Eritrea, Ethiopia and Somalia are collectively known as the Horn of Africa. The area is the easternmost projection of the African continent. Africa’s highest mountains, Mount Kilimanjaro and Mount Kenya are located in East Africa. The region also includes the world's second largest freshwater lake, Lake Victoria, and the world's second deepest lake, Lake Tanganyika.

In 2019, East Africa had a population of about 364.4 million people. It has been the continent’s fastest-growing region in recent years, mostly driven by above average growth in Ethiopia, Djibouti, Kenya, Rwanda, Tanzania and Uganda. In 2020, Tanzania transitioned from low-income to middle-income status, joining Kenya, Comoros and Djibouti in the World Bank’s lower middle-income category. The region’s growth rate averaged 5.6% in 2015 and 4.5% in 2019, compared with 2.9% and 2.5% in Africa overall. The COVID-19 pandemic, however, led to a sharp drop in growth but did not cause a recession. The economic profile of the 12 countries of East Africa differ significantly. Comoros, for example, is highly dependent on tourism, and tourism is an important economic sector for Kenya, Tanzania, and Uganda as well. Kenya’s and Ethiopia’s economies are generally more diversified; and Tanzania has diversified export baskets. On the other hand, 5 out of 12 countries are landlocked.
Overall, many structural challenges to development persist. Those include poor infrastructure connectivity, especially in transport and power; persistent non-tariff barriers to trade; insufficient economic diversification, poorly implemented regional agreements; and weak capacity within the regional economic institutions. Even in the faster growing economies, inclusive growth remains a challenge.

The 12 countries of East Africa have overlapping memberships in four African Union-recognised regional economic communities. The latter are the Common Market for Eastern and Southern Africa (COMESA), the East African Community (EAC), the Intergovernmental Authority on Development (IGAD) and the Southern African Development Community (SADC). Other regional blocs to which some of the countries belong are the Indian Ocean Community, the Economic Community of Great Lakes Countries, and the Economic Community of Central African States.

East Africa has a young and fast-growing population. Coming from a baseline of about 163 million people in 1990, by 2019 its population had more than doubled to 365.7 million people. Over the coming two decades, East Africa’s population is expected to grow by more than 70% and reach 631.3 million people. Population growth in East Africa is driven by natural population growth, in other words, births outstripping deaths.

East Africa’s population is young with half of it being younger than 18.6 years old and 29% being under 15 in 2019. The region’s median age is slightly higher than West Africa’s at 18.3 years but significantly lower than Southern Africa’s at 21.6 years or North Africa’s at 26.7 years. Central Africa has the youngest population on the continent with a median age of 17.2 years.

On the Current Path, East Africa’s demographic structure is expected to change slowly but steadily. By 2043, only 22% of the population is forecast to be under 15 years old, down by 7 percentage points. In 2019, average total fertility in East Africa stood at 4.5 births per woman. By 2043, it is expected to drop to 3 births per woman. As a consequence, the median age is projected to increase to 23.6 years, the third highest on the continent after North and Southern Africa. Within East Africa, Somalia, Uganda and Burundi have the youngest population with median ages below 17 years. Djibouti, on the other extreme, has a median age of 26.2, almost as high as North Africa’s.

In 2019, East Africa’s average life expectancy was 65.7 years. By 2043, the average East African is expected to add more than 6 years and live for 72.1 years. Within East Africa, the picture is again very heterogeneous. At 69.1 years, Rwanda has the highest life expectancy, followed by South Sudan at 68.9 years. Somalia’s and South Sudan’s average life expectancy, on the other extreme, is more than ten years lower than Rwanda’s.

With 6.7 deaths per 1 000 people in 2019, East Africa’s communicable-disease burden is the second lowest on the continent although North Africa’s is more than 6 times lower. Southern Africa, Central Africa and West Africa have communicable-disease burdens of 4.5, 4.8 and 5 deaths per 1 000 people, respectively. By 2023, the death rate for non-communicable diseases will be about the same as for communicable diseases in East Africa: 2.9 per 1 000 people.

A higher life expectancy would boost East Africa’s workforce. By 2043, the region’s working-age population is expected to account for about 62.3% of the population compared to 55.2% in 2019. Indeed, the ratio of people of working age relative to the dependent population is improving, but not fast enough. On the Current Path, East Africa is expected to reach the peak of its demographic ‘sweet spot’ only in 2065 (from 1.2 in 2019 to 2.1 in 2062).

East Africa is the least urbanised region on the continent. In 2019, only 26.8% of East Africans lived in cities and towns while 73.2% lived in rural areas. In comparison, West and Southern Africa’s rates of urbanisation already reach 46.6% and 45.7%, respectively, and in Central Africa 49.9% of the population lives in urban areas. East Africa too will become more urbanised in the future, but it will continue to lag behind its peer regions. By 2043, 37.5% of East Africans are expected to live in urban areas versus 58.9% of Central Africans. The anticipated ratio for Africa’s low-income economies is 40.7% urban versus 59% rural.

East Africa’s population of about 364.4 million people is unevenly distributed across the countries of the region and within countries as well. In Rwanda, for example, population density is the highest with 5.11 people per hectare in 2019, followed by Comoros and Burundi with 4.6 and 4.3 people per hectare, respectively. South Sudan, on the other extreme of the spectrum, had a population density of 0.16 people per hectare in 2019, the lowest in the region. In 2019, East Africa was the second most densely populated region on the continent with a density of about 0.63 people per hectare. East Africa is preceded by West Africa with an average population density of 0.78 people per hectare.

Population density in Southern Africa and North Africa is around 0.3 people per hectare, and in Central Africa it is about 0.27 people per hectare. Population growth means that population density in East Africa is forecast to increase by more than 70% reaching 1.08 people per hectare. West Africa will remain the most densely populated region on the continent.

East Africa’s biggest urban centres are Addis Ababa in Ethiopia, Nairobi in Kenya, Dar es Salaam in Tanzania, and Mogadishu in Somalia.

Between 1990 and 2019, East Africa’s GDP expanded from US$85 billion to US$352.1 billion. In 2019, East Africa had the second lowest GDP on the continent, following Southern Africa, West Africa and North Africa with the highest GDP. Only Central Africa’s GDP is lower. Nevertheless, East Africa’s growth potential is high. In 2043, the region is forecast to have the third highest GDP on the continent at a value of US$1 650.3 billion, almost five times as large as in 2019.

Within East Africa, the size of the economies is very heterogeneous. Sudan, Ethiopia, Kenya, Tanzania and Uganda (in this order) are East Africa’s largest economies. Together, they accounted for more than 90% of the region’s GDP in 2019. Comoros and Djibouti are the region’s smallest economies. Ethiopia is the country with the biggest growth potential over the coming decades. By 2043, its economy is forecast to have a value of US$588.6 billion. Ethiopia alone would account for more than a third of East Africa’s GDP in 2043.

Despite the anticipated expansion of East Africa’s economies, growth is constrained by poor infrastructure, unreliable power, low agricultural productivity, poor governance, and a lack of market competitiveness.

Although many of the charts in the sectoral scenarios also include GDP per capita, this overview is an essential point of departure for interpreting the general economic outlook of East Africa.

In 2019, East Africa’s GDP average per capita was US$2 320. On the Current Path, average per capita income is expected to more than double reaching US$5 206 by 2043. East Africa comprises 12 economies of which four are classified as lower middle-income: Comoros, Djibouti, Kenya and Tanzania. Burundi, Eritrea, Ethiopia, Somalia, South Sudan, Sudan, Rwanda and Uganda all fall into the low-income category. Therefore, the range of GDP per capita income is quite broad with Djibouti having the highest GDP per capita at US$4 005 and Burundi the lowest at US$711 in 2019.

In 2019, East Africa’s GDP per capita was the lowest on the continent. North Africa’s was the highest, followed by Southern Africa, West Africa and Central Africa. In 2043, however, East Africa will have a higher average GDP per capita than Central Africa.

In 2019, East Africa’s informal sector accounted for approximately 28.2%. By 2043, the region’s informal sector is forecast to account for 24.8%, an improvement that likely reflects improvements in overall state capacity, including for taxation. Within East Africa, Tanzania has the largest informal sector accounting for 45% of GDP, followed by Somalia with an informal sector that accounts for 40.5% of GDP. Sudan and Kenya, on the other hand, have much smaller informal sectors representing 14% and 22% of GDP, respectively.

The IFs platform uses data from the Global Trade and Analysis Project (GTAP) to classify economic activity into six sectors: agriculture, energy, materials (including mining), manufacturing, services and information and communication technologies (ICT). Most other sources use a threefold distinction between only agriculture, industry and services with the result that data may differ.

In 2019, East Africa’s service sector accounted for half of the region’s GDP (50.4%), followed by the agriculture sector which represented about 28.1% and manufacturing at 11.6%. In the future, the service sector is expected to remain the most important contributor to East Africa’s GDP. Its share is set to grow to 60.8% by 2043. At the same time, the contribution of the agriculture sector is forecast to significantly drop to 9.3%. The contribution of the manufacturing sector to GDP, on the other hand, is forecast to increase by about 6 percentage points to 17.7% in 2043. In 2019, the contribution of ICT accounted for 4.9% of East Africa’s GDP and is expected to represent 7.3% of GDP in 2043. The importance of the energy sector, on the other hand, is set to drop from accounting for 3.6% of GDP in 2019 to 2.5% in 2043. By then, the materials sector is going to account for 2.5%, up from 1.3% in 2019.

In 2019, East Africa’s service sector was worth US$177.3 billion. By 2043, it will be almost six times as large and worth US$1003.3 billion. Services will be followed by the manufacturing sector at a value of US$291.8 billion in 2043, up from US$40.9 billion in 2019.

In 2019, East Africa had the second smallest service sector with Central Africa having the smallest in absolute terms and West Africa having the largest. At a volume of US$426.1 billion, West Africa’s service sector was 2.4 times as large as East Africa’s. However, by 2043 East Africa’s service sector is forecast to be the second largest on the continent leaving behind both North Africa and Southern Africa.

The data on agricultural production and demand in the IFs forecasting platform initialises from data provided on food balances by the Food and Agriculture Organization (FAO). IFs contains data on numerous types of agriculture but aggregates its forecast into crops, meat and fish, presented in million metric tons. Chart 9 shows agricultural production and demand as a total of all three categories.

In 2019, East Africa’s agricultural production amounted to about 244 million tons, just short of 15.5 million tons to match demand at 259.5 million tons. Going forward, this gap is going to widen. By 2043, it will be as large as 134 million tons; almost nine times as large as in 2019. Total production will be up to 396.5 million tons, demand will be 535.2 million tons.

East Africa is the second largest agricultural producer after West Africa with an output of 380.5 million tons in 2019. West Africa is set to experience an even greater gap between agricultural production and demand than East Africa.

Within East Africa, the largest agricultural producers are Ethiopia, Tanzania and Kenya with production levels of 57.1, 51.2 and 35 million tons each. Ethiopia alone accounts for almost a quarter of agricultural production in East Africa. By 2043, it will be responsible for more than 30% of the region’s agricultural output.

There are numerous methodologies for and approaches to defining poverty. We measure income poverty and use GDP per capita as a proxy. In 2015, the World Bank adopted the measure of US$1.90 per person per day (in 2011 international prices), also used to measure progress towards the achievement of Sustainable Development Goal (SDG) 1 of eradicating extreme poverty. To account for extreme poverty in richer countries occurring at slightly higher levels of income than in poor countries, the World Bank introduced three additional poverty lines in 2017:

• US$3.20 for lower middle-income countries 
• US$5.50 for upper middle-income countries
• US$22.70 for high-income countries.

With most of East Africa’s countries falling into the low-income category, the region uses the US$1.90 benchmark to define extreme poverty. In 2019, 35% of the population was living below the poverty line, which corresponds to 127.5 million people. East Africa has the second lowest poverty rate on the continent, preceded by Southern Africa, West Africa and Central Africa with the highest rate. In fact, Central Africa’s poverty rate is almost 20 percentage points higher than East Africa’s. On the Current Path, the number of East Africans living in extreme poverty will drop to 71.5 million people by 2043, and the region’s poverty rate is projected to significantly decline to 12.2% by then. Within East Africa, however, poverty rates vary greatly between countries. South Sudan, Burundi, Somalia and Rwanda have the largest share of the population living in extreme poverty, each exceeding 50%, and in the case of conflict-ridden South Sudan and Burundi even close to 80% of the population. On the other extreme, hardly 12% of Sudanese live in extreme poverty, and roughly 20% and 25% of the populations of Comoros and Djibouti, respectively.

The IFs platform forecasts six types of energy, namely oil, gas, coal, hydro, nuclear and other renewables. To allow comparisons between different types of energy, the data is converted into billion barrels of oil equivalent (BBOE). The energy contained in a barrel of oil is approximately 5.8 million British thermal units (MBTUs) or 1 700 kilowatt-hours (kWh) of energy.

In 2019, East Africa’s total energy production was about 440 million barrels of oil. By 2043, it is estimated to increase more than threefold to 1 439 million barrels. The region’s current energy mix is dominated by oil which accounted for 41% of total production in 2019. Oil was followed by hydro at 25%, gas at 18%, and other renewables at 14% of total production. Coal only accounted for 3% in 2019.

On the Current Path, renewables are going to become more dominant, accounting for 61% of total energy production in 2043. Oil and hydro are set to drop to account for 12% and 13%, respectively, and gas is also forecast to become less relevant in the energy mix representing 13% of total energy production in 2043.

Carbon is released in many ways, but the three most important contributors to greenhouse gases are carbon dioxide (CO2), carbon monoxide (CO) and methane (CH4). Since each has a different molecular weight, IFs uses carbon. Many other sites and calculations use CO2 equivalent.

East Africa’s carbon emissions were 23 million tons of carbon in 2019. They are forecast to increase almost fivefold 104 million tons of carbon emissions by 2043. In 2019, East Africa was the second least carbon emitter in Africa, only next to Central Africa owing to its relatively low industrialisation. North Africa and Southern Africa are by far the greatest emitters at 169 and 147 million tons, respectively, compared to only 23 million tons for East Africa. Within the region, Sudan and Tanzania are the greatest emitters at 6.5 and 5.7 million tons, respectively, followed by Kenya at 3.5 million tons and Uganda at 2.8 million tons. With the smallest economy, Comoros was also the smallest carbon emitter in 2019 at less than a million tons.

The Stability scenario represents reasonable but ambitious reductions in risk of regime instability and lower levels of internal conflict. Stability is generally a prerequisite for other aspects of development and this would encourage inflows of foreign direct investment (FDI) and improve business confidence. Better governance through the accountability that follows substantive democracy is modelled separately.

The intervention is explained here in the thematic part of the website.

The Stability scenario reflects significant interventions, including increasing regime stability, lowering levels of internal conflict, improving gender empowerment and addressing high levels of corruption.
In 2019, East Africa scored 0.67 on the governance security index; slightly lower than West Africa with a score of 0.7 but higher than Central Africa that scored 0.6. In the Stability scenario, governance security in East Africa is projected to improve to a score of 0.85 by 2043 compared to 0.75 on the Current Path.

Within East Africa, Kenya, Comoros, Rwanda and Tanzania are the best performing countries on governance security. Somalia and South Sudan have the lowest scores. By 2043, the Stability scenario will improve governance security the most in Somalia (0.159), followed by Sudan (0.148), South Sudan (0.139), and Burundi (0.115).

In 2019, East Africa’s GDP per capita was US$2 320. The Stability scenario can lead to a higher GDP per capita of US$5 415 by 2043 compared to the Current Path forecast of US$5 206. Within East Africa, Sudan and Ethiopia will gain the most from the Stability scenario of US$298 and US$281 additions to GDP per capita, whereas Burundi gains only US$57 from the Stability scenario.

The Stability scenario has the potential to reduce the number of people living in extreme poverty by almost 6 million people compared to the Current Path (65.7 versus 71.5 million by 2043).

In the Stability scenario, East Africa’s poverty rate could drop from 35% in 2019 to 11.2% in 2043 compared to 12.2% in the Current Path forecast. In other words, with the interventions included in the Stability scenario, East Africa could speed up its poverty reduction efforts even if not by a great margin. Obviously, the impact of the intervention will vary greatly across the region with the countries most affected by violent conflict reaping the greatest gains. Somalia will see an additional 1.1 million people lifted out of poverty as a result of the Stability scenario in 2043 compared to the Current Path forecast, second only to Tanzania of 1.3 million people.

This section presents the impact of a Demographic scenario that aims to hasten and increase the demographic dividend through reasonable but ambitious reductions in the communicable-disease burden for children under five, the maternal mortality ratio and increased access to modern contraception. 

The intervention is explained here in the thematic part of the website.

Demographers typically differentiate between a first, second and even a third demographic dividend. We focus here on the contribution of the size of the labour force (between 15 and 64 years of age) relative to dependants (children and the elderly) as part of the first dividend. A window of opportunity opens when the ratio of the working-age population to dependants is equal to or surpasses 1.7.

Increasing access to modern contraception will bring down East Africa’s total fertility rate more quickly than on the Current Path: from 4.5 births per woman in 2019 to 2.3 in 2043 versus 2.9 births on the Current Path. Lowering the fertility rate more quickly than on the Current Path would slow down East Africa’s population growth and bring about a somewhat faster change in the population age structure. The latter will result in a more favourable ratio between people of working age and dependants, especially children. Thanks to the interventions in the Demographic scenario, East Africa has the potential to accelerate its demographic transition by increasing the ratio of workers to dependants from 1.2 in 2019 to 1.9 in 2043 compared to 1.6 on the Current Path.

Within East Africa, in 2019 the spectrum for total fertility ranged from 6 births per woman in Somalia to 2.8 births per woman in Djibouti. Other than Somalia, Burundi and Uganda also have average total fertility rates surpassing 5 births per woman in 2019. After Djibouti, which can be considered an outlier, Kenya and Rwanda have the lowest fertility rates at 3.5 and 4 births per woman, respectively.

The infant mortality rate is the number of infant deaths per 1 000 live births and is an important marker of the overall quality of the health system in a country.

At 41.8 infant deaths per 1 000 live births in 2019, infant mortality in East Africa is high although it has improved greatly since 1990 when it stood at a rate of 98.5. West Africa, however, has even higher infant mortality at 58.6 infant deaths per 1000 live births. By 2043, East Africa’s infant mortality rate is forecast to drop by more than 50% to 19 infant deaths per 1 000 live births on the Current Path. The interventions in the Demographic scenario could reduce that rate even further to 15.5%, reducing infant mortality by an additional 3.5 deaths per 1 000 live births. In comparison, by 2043 East Africa will only gain more from interventions in the Demographic scenario than North Africa — Southern, West, and Central Africa will all see an additional reduction in mortality rates by more than 4.5 deaths per 1 000 live births. Within East Africa, South Sudan and Somalia will gain the most from the Demographic scenario by 2043, reducing infant mortality rates by an additional 10.1 and 6.3 deaths compared to the Current Path forecast.

In 2019, East Africa’s average GDP per capita was US$2 320. By 2043, the Demographic scenario could push it to US$5 414 versus US$5 206 on the Current Path. The additional gains in GDP per capita as a result of the Demographic scenario accrue from the benefits of the demographic dividend that East Africa is set to reap 6 years earlier than in the Current Path forecast.

Compared to the Current Path, the Demographic scenario could reduce the number of people living in extreme poverty by close to 6 million people in 2043. An expected total of 65.7 million people, or 11.2%, of the population would be living below the poverty line by 2043 compared to 12.2% in the Current Path.

This section presents reasonable but ambitious improvements in the Health/WaSH scenario, which include reductions in the mortality rate associated with both communicable diseases (e.g. AIDS, diarrhoea, malaria and respiratory infections) and non-communicable diseases (NCDs) (e.g. diabetes), as well as improvements in access to safe water and better sanitation. The acronym WaSH stands for water, sanitation and hygiene.

The intervention is explained here in the thematic part of the website.

The Health/WaSH scenario has the potential to increase life expectancy in East Africa from 65.7 years in 2019 to 72.5 years in 2043 versus 72.1 years on the Current Path. East Africa has the highest life expectancy in sub-Saharan Africa and the second highest in Africa. At 74.5 years, only North Africa has a higher life expectancy on the continent. The average Southern and Central African lives almost 4 years less than the average East African. Of course, the picture varies across countries, and average life expectancy within East Africa ranges from 69.1 years in Rwanda to only 58.5 in Somalia.

The Health/WaSH scenario would have a positive impact on East Africa’s infant mortality rate. The latter could drop from 41.8 in 2019 to 17.4 deaths per 1 000 live births in 2043 compared to an expected rate of 19 deaths on the Current Path. South Sudan will benefit by far the most from Health/WaSH scenario within East Africa at 7.2 deaths per 1 000 live births.

The Agriculture scenario represents reasonable but ambitious increases in yields per hectare (reflecting better management and seed and fertiliser technology), increased land under irrigation and reduced loss and waste. Where appropriate, it includes an increase in calorie consumption, reflecting the prioritisation of food self-sufficiency above food exports as a desirable policy objective.

The intervention is explained here in the thematic part of the website.

The data on yield per hectare (in metric tons) is for crops but does not distinguish between different categories of crops.

In 2019, crop yields in East Africa stood at 3 metric tons per hectare. According to the Current Path forecast, by 2043 crop yields in East Africa will modestly increase to 3.8 metric tons per hectare — an increase by almost 27%. In the Agriculture scenario, on the other hand, yields could increase by about 116% over the same time period and reach 6.5 metric tons per hectare by 2043.

Again, the region is very heterogeneous when it comes to agricultural productivity. Crop yields per hectare range from 28.6 in Djibouti as an outlier to 8.3 in Rwanda to 1.4 in Sudan.

East Africa performs the worst on agricultural productivity measured in crop yields per hectare on the continent. North Africa performs the best at 6.5 metric tons per hectare, followed (even though not closely) by Southern Africa at 4.5 metric tons in 2019. In the Agriculture scenario, East Africa will continue to rank last on agricultural productivity measured in crop yields per hectare, with measured implication for food security in the region. The East African Agricultural Productivity Programme (EAAPP) is a regional partnership of the governments of Kenya, Ethiopia, Tanzania and Uganda to boost agricultural productivity by establishing regional centres of excellence for agricultural research and investing in commodities of sub-regional importance to mitigate food insecurity.[1ASARECA, The East African Agricultural Productivity Programme (EAAPP)]

In 2019, net imports accounted for 8.2% of East Africa’s agricultural demand. On the Current Path, agricultural demand is increasingly outpacing production which will lead to greater import dependence. By 2043, net imports are expected to account for 26% of agricultural demand. The Agriculture scenario has the potential to increase production and reduce import dependence to meet the rapid increase in demand fuelled by population growth. In that scenario, East Africa could achieve a surplus in production with import dependence dropping to −11.7%.

Within East Africa, agricultural import dependence varies among countries. Djibouti, for example, required more than 90% of net imports to cover agricultural demand in 2019, but the country is an outlier. At close to 40%, Somalia ranked second in import dependence, followed by Sudan and Kenya at 12% and 11.4%, respectively. The remaining countries were less dependent on agricultural imports.
From an aggregate point of view, East Africa is the only region that could free itself from import dependence in the Agriculture scenario. All the other regions would be able to reduce import dependence compared to the Current Path but remain dependent on net imports to cover agricultural demand. North Africa is the region with the highest anticipated import dependence by far: 33.8% on the Current Path and 26.5% in the Agriculture scenario.

The Agriculture scenario is expected to push East Africa’s GDP per capita to US$5 607 by 2043 compared to the Current Path forecast of US$5 206. At individual country level, Tanzania and Sudan will gain the most from the Agriculture scenario, adding US$886 and US$600 to their GDP per capita in 2043 compared to the Current Path forecast. Djibouti on the other hand will see a reduction in GDP per capita by US$17 as diminishing returns to land decreases total productivity.

The impact of the interventions in the Agriculture scenario on poverty in East Africa is significant. It implies an important reduction in the share of the population living below the poverty line in 2043: 8.6% instead of 12.2% in the Current Path forecast. The Agriculture scenario has the potential to prevent more than 20 million people falling into poverty by 2043 — the anticipated total being 50.1 million people compared to 71.5 million on the Current Path forecast.

The Education scenario represents reasonable but ambitious improved intake, transition and graduation rates from primary to tertiary levels and better quality of education. It also models substantive progress towards gender parity at all levels, additional vocational training at secondary school level and increases in the share of science and engineering graduates.

The intervention is explained here in the thematic part of the website.

Education is key to development but improvements in education typically take a long time to show results. With a mean of 5.2 years of education among the adult population in 2019, East Africa’s educational outcomes are below the average for sub-Saharan Africa which is 6 years. Women have on average 4.7 years of education, one year less than men which is significant. Via the Education scenario, East Africa’s mean years of education could increase to 7.3 years in 2043. This represents an improvement of 0.4 years compared to the Current Path forecast of 6.9 years over the same time period. Male education outcomes would still be better than those for female education: 7.6 versus 7 mean years of education, respectively.

In 2019, East Africa’s primary test score was 29%, slightly worse than the average score of 30.9% for sub-Saharan Africa. According to the Current Path forecast, East Africa’s performance will improve to 31.7% in 2043 versus 32.7% for sub-Saharan Africa. The Education scenario is expected to accelerate improvements, pushing average test scores for primary learners in East Africa to 36.9% by 2043; an increase of just over 5 percentage points compared to the Current Path forecast for 2043.

In the Education scenario, the test score at the secondary level could increase by almost 10 percentage points from 37.6% in 2019 to 47.2% in 2043 versus 39.7% on the Current Path. In both scenarios, East Africa will perform above the average for sub-Saharan African.

By 2043, East Africa’s GDP per capita will increase to US$5 206 on the Current Path versus US$5 381 in the Education scenario, a difference of US$175.

In the Education scenario, it is expected that 10.8% of East Africa’s population will live in extreme poverty by 2043, down from 35% in 2019 and compared to 12.2% in the Current Path forecast. This translates to a projected total of 63 million poor people in 2043 compared to 71.5 million in the Current Path forecast or 8.5 million people escaping extreme poverty. By 2043, the reduction in extreme poverty rates as a result of the Education scenario, compared to the Current Path forecast, ranges from 3 percentage points in Tanzania to 0.7 in Ethiopia.

The Manufacturing/Transfers scenario represents reasonable but ambitious manufacturing growth through greater investment in the economy, investments in research and development, and promotion of the export of manufactured goods. It is accompanied by an increase in welfare transfers (social grants) to moderate the initial increases in inequality that are typically associated with a manufacturing transition. To this end, the scenario improves tax administration and increases government revenues.

The intervention is explained here in the thematic part of the website.

Chart 30 should be read with Chart 8 that presents a stacked area graph on the contribution to GDP and size, in billion US$, of the Current Path economy for each of the sectors.

In the Manufacturing/Transfers scenario, the manufacturing sector will experience the largest gain in terms of its relative contribution to GDP relative to the Current Path. Its contribution is expected to have increased by 0.43 percentage points in 2043 after having reached a peak of an additional 0.61 percentage points around 2037. The manufacturing sector is followed by the service sector with an increase of 0.24 percentage points in its relative contribution to GDP. The agriculture sector is forecast to drop in importance losing 0.62 percentage points relative to the Current Path. The materials and ICT sectors are both set to add 0.4 and 0.3 percentage points, and the energy sector is expected to lose 0.13 percentage points relative to the Current Path.

The value of East Africa’s service sector could be US$87.7 billion larger in the Manufacturing/Transfers scenario compared to the Current Path. Manufacturing could contribute an extra US$32 million in this scenario, followed by ICT at an additional US$10.5 million.

In the Manufacturing/Transfers, scenario government to household welfare transfers are forecast to increase from US$14.8 billion in 2019 to US$135.4 billion in 2043 versus 83.8 billion on the Current Path. In other words, social transfers would be more than 60% larger than on the Current Path. Compared to the Current Path, additional gains in welfare transfers (as a percentage of GDP) in the Manufacturing scenario in 2043 range from 5% in Uganda to 1% in Burundi. East Africa, at 2.5 percentage points, will make the greatest gains in welfare transfers (as a percentage of GDP) in 2043 due to Manufacturing/Transfers scenario interventions within sub-Saharan Africa, next only to North Africa at an average of 2.6 percentage points.

East Africa’s GDP per capita is expected to grow to US$5 489 in the Manufacturing/Transfers scenario compared to US$5 206 on the Current Path.

The Manufacturing/Transfers scenario has the potential to reduce the share of the population living in extreme poverty from 35% in 2019 to 10.2% in 2043 compared to 12.2% in the Current Path forecast. This is a 2 percentage point improvement that translates to close to 12 million people escaping poverty in 2043 via the interventions in the Manufacturing/Transfers scenario.

The Leapfrogging scenario represents a reasonable but ambitious adoption of and investment in renewable energy technologies, resulting in better access to electricity in urban and rural areas. The scenario includes accelerated access to mobile and fixed broadband and the adoption of modern technology that improves government efficiency and allows for the more rapid formalisation of the informal sector.

The intervention is explained here in the thematic part of the website.

Fixed broadband includes cable modem Internet connections, DSL Internet connections of at least 256 KB/s, fibre and other fixed broadband technology connections (such as satellite broadband Internet, ethernet local area networks, fixed-wireless access, wireless local area networks, WiMAX, etc.).

In 2019, East Africa, like most of low-income Africa, had a low fixed broadband rate of 2.7 subscriptions per 100 people. In the Leapfrogging scenario, by 2043, fixed broadband is set to increase to 47.7 subscriptions per 100 people versus 29.2 on the Current Path. Individual country heterogeneity exists within East Africa, as 6 of 12 East African nations will additionally add more than 20 subscriptions per 100 people in the Leapfrogging scenario compared to the Current Path in 2043, while Djibouti adds the least at 9 subscriptions per 100 people. On average, gains in fixed broadband subscriptions in the Leapfrogging scenario compared to the Current Path, East Africa performs only better than North Africa at 16.6 additional subscriptions per 100 people by 2043.

Mobile broadband refers to wireless Internet access delivered through cellular towers to computers and other digital devices.

In 2019, East Africa had 25.7 mobile broadband subscriptions per 100 people. The Leapfrogging scenario has the potential to push mobile broadband subscriptions to 144.9 subscriptions per 100 people by 2043. However, even on the Current Path the region is expected to reach 140.9 subscriptions by then. The greatest benefit of the interventions of the Leapfrogging scenario plays out in the medium term when projected subscriptions are indeed tangibly higher than on the Current Path. In other words, mobile broadband subscriptions in East Africa are expected to increase rapidly either way but more quickly in the Leapfrogging scenario.

Access to electricity remains a key challenge for East Africa. In 2019, 43.6% of the population had access to electricity. On the Current Path, 73.4% of the population will have access to electricity in 2043. This means that East Africa as a region will miss the SDG target (Indicator 7.1.1) of universal electricity access (98%) by 2030. In the Leapfrogging scenario, access to electricity is projected to expand faster, giving 88.2% of the population access by 2043.

In the Leapfrogging scenario, East Africa’s GDP per capita is expected to experience a larger increase than on the Current Path: from US$2 320 in 2019 to US$5 575 compared to US$5 206 in the Current Path.

The interventions in the Leapfrogging scenario are projected to benefit poverty reduction efforts in East Africa. The share of the population living below the poverty line could drop from 35% to 10.5% by 2043 compared to 12.2% on the Current Path trajectory. When assessing absolute numbers, the Leapfrogging scenario would reduce the number of people living in poverty to 61.6 million versus the projected 71.5 million on the Current Path forecast. In other words, almost 10 million people would escape poverty in the Leapfrogging scenario.

The Free Trade scenario represents the impact of the full implementation of the African Continental Free Trade Area (AfCFTA) by 2034 through increases in exports, improved productivity and increased trade and economic freedom.

The intervention is explained here in the thematic part of the website. 

The trade balance is the difference between the value of a country's exports and its imports. A country that imports more goods and services than it exports in terms of value has a trade deficit, while a country that exports more goods and services than it imports has a trade surplus.

In 2019, East Africa had a trade deficit that accounted for 11% of GDP. In the Free Trade scenario, the region’s trade balance is set to improve with the deficit accounting for 3.8% of GDP by 2038 before it starts growing again arriving at 5.9% in 2043 versus 4.2 in the Current Path. Essentially, in any case, East Africa is expected to have a negative trade balance by 2043, but in the Free Trade scenario the deficit would be higher than in the Current Path.

However, the implementation of the AfCFTA in the Free Trade scenario would improve the short- and medium-term trade deficit for East Africa compared to the Current Path forecast.

In 2019, Burundi and South Sudan were the countries with the largest trade deficits accounting for −24.6% and −17.5% of GDP each. Sudan was the country with the smallest deficit at −5.3% of GDP. The Free Trade scenario would improve Sudan’s trade deficit to −3.3% of GDP.

In the Free Trade scenario East Africa’s GDP per capita is expected to experience the largest increase among the sectoral scenarios: from US$2 320 in 2019 to US$5 869 versus US$5 206 on the Current Path.

Trade openness will reduce poverty in the long term after initially increasing it due to the redistributive effects of trade. Most African countries export primary commodities and low-tech manufacturing products, and therefore a continental free trade agreement (AfCFTA) that reduces tariffs and non-tariff barriers across Africa will increase competition among countries in primary commodities and low-tech manufacturing exports. Countries with inefficient, high-cost manufacturing sectors might be displaced as the AfCFTA is implemented, thereby pushing up poverty rates. In the long term, as the economy adjusts and produces and exports its comparatively advantaged (lower relative cost) goods and services, poverty rates will decline.

In the Free Trade scenario, extreme poverty in East Africa is expected to decrease more rapidly than on the Current Path. By 2043, 10.2% of people are forecast to live in extreme poverty in the Free Trade scenario compared to 12.2% in the Current Path forecast. The difference translates into close to 12 million people that would be able to escape poverty in the Free Trade scenario.

The Financial Flows scenario represents a reasonable but ambitious increase in worker remittances and aid flows to poor countries, and an increase in the stock of foreign direct investment (FDI) and additional portfolio investment inflows to middle-income countries. We also reduced outward financial flows to emulate a reduction in illicit financial outflows.

The intervention is explained here in the thematic part of the website.

At 5.4% of GDP in 2019, foreign aid played a more important role for East Africa than for the average economy in sub-Saharan Africa in which aid accounted for just over 3%. In both the Current Path and the Financial Flows scenario, the contribution of foreign aid to East Africa’s economy, just as in all other regions within Africa, is projected to become less significant by 2043 as the continent shifts away from aid towards investment and trade, dropping to 2.2% of GDP in the Financial Flows scenario and 2.1% on the Current Path. The average for sub-Saharan Africa is expected to be 1.5% of GDP in the Current Path and 1.6% in the Financial Flows scenario. In the latter, aid will play a relatively more important role for Burundi, South Sudan and Rwanda with foreign aid accounting for 13.8%, 31.1% and 8.2%, respectively.

FDI flows to East Africa accounted for about 3.1% of GDP in 2019 versus the average of 2.7% for sub-Saharan Africa. The COVID-19 pandemic meant a drastic drop in FDI inflows to East Africa down to 1.4% of GDP in 2020. In the Financial Flows scenario, FDI as a share of GDP is projected to recover and surpass pre-pandemic levels. By 2043, flows are set to account for 3.9% of East Africa’s GDP compared to 3.5% in the Current Path forecast. The countries in which FDI inflows are forecast to account for the highest share of GDP are Djibouti (7.3%), Uganda (5.5%), Eritrea (5.3%), Sudan (4.8%) and Tanzania (4.6%). In South Sudan and Burundi, FDI is expected to only account for 0.8% and 1.7%, respectively.

In 2019, remittances accounted for 1.3% of East Africa’s GDP. On the Current Path, this figure will increase to 2.2% by 2043 — an increase by close to 70%. In the Financial Flows scenario, remittances are expected to account for 2.4% of the region’s GDP. In absolute terms, remittances will amount to US$36.5 billion on the Current Path and US$41.2 billion in the Financial Flows scenario, up from US$4.9 billion in 2019.

Both in absolute and in relative terms, East Africa is the region in sub-Saharan Africa that received the second largest volume of remittances after West Africa. The latter had an inflow of US$26.6 billion in 2019, more than five times East Africa’s volume.

In the Financial Flows scenario, East Africa’s GDP per capita is expected to gain US$85 more than on the Current Path reaching US$5 291 in 2043.

The interventions in the Financial Flows scenario have the potential to modestly reduce the share of East Africa’s population living in extreme poverty to 11.5% by 2043 compared to 12.2% in the Current Path forecast. This means that 28.5 instead of 71.5 million people could live below the poverty line in 2043.

The Infrastructure scenario represents a reasonable but ambitious increase in infrastructure spending across Africa, focusing on basic infrastructure (roads, water, sanitation, electricity access and ICT) in low-income countries and increasing emphasis on advanced infrastructure (such as ports, airports, railway and electricity generation) in higher-income countries.

Note that health and sanitation infrastructure is included as part of the Health/WaSH scenario and that ICT infrastructure and more rapid uptake of renewables are part of the Leapfrogging scenario. The interventions there push directly on outcomes, whereas those modelled in this scenario increase infrastructure spending, indirectly boosting other forms of infrastructure, including those supporting health, sanitation and ICT. 

The intervention is explained here in the thematic part of the website.

In 2019, only 159.6 million people in East Africa had access to electricity, accounting for about 43.6% of the population. In urban areas, the access rate was 69.3%, which was essentially twice as high as in rural areas (34.7%). The interventions in the Infrastructure scenario have the potential to increase East Africa’s overall electricity access rate to 77% by 2043 compared to 73.4% on the Current Path. This means that about 22.8 million more people could benefit from access to electricity by 2043.
Rural areas would benefit more from the interventions in the Infrastructure scenario than urban areas because they are coming from a lower baseline. Access rates in rural areas would increase to 70.7% by 2043 compared to 66% on the Current Path. In urban areas, the Infrastructure scenario accounts for an additional improvement of about 2 percentage points, pushing the expected access rate to 85.8%.

In 2019, electricity access in East Africa was higher than in Central Africa but lower than in West Africa and Southern Africa. Through interventions in the Infrastructure scenario, overall access rates would be on par in East and West Africa by 2043. There is wide heterogeneity in access to electricity in East Africa, with rates ranging from 76% in Comoros to just 9% in Burundi. Conflict-ridden states of Somalia, South Sudan and Burundi suffer from low access to electricity. By 2043, interventions in the Infrastructure scenario will benefit South Sudan and Somalia most at 14.8 and 9 more percentage points, respectively, compared to the Current Path.

Indicator 9.1.1 in the Sustainable Development Goals refers to the proportion of the rural population who live within 2 km of an all-season road and is captured in the Rural Access Index.

Investments in rural road infrastructure typically have positive socio-economic impacts, such as increased rural incomes and poverty reduction, improved maternal and paediatric health outcomes and heightened agricultural productivity. In 2019, 52% of East Africa’s rural population had access to an all-weather road within a distance of 2 km. This is about 2 percentage points above the average access rate of sub-Saharan Africa. The relatively high baseline explains the relatively modest impact of the Infrastructure scenario on rural road access. By 2043, it is projected that 63% of East Africa’s rural population will have access to an all-weather road within a distance of 2 km compared to 60.5% in the Current Path forecast.

Within sub-Saharan Africa, East Africa has better rural road infrastructure than Central Africa and West Africa but worse than Southern Africa.

Improvements included in the Infrastructure scenario are expected to push GDP per capita from US$2 320 in 2019 to US$5 382 in 2043, US$176 above the Current Path forecast.

In the Infrastructure scenario, the share of East Africans living in extreme poverty is expected to drop from 35% in 2019 to 11.6% in 2043 versus 12.2% on the Current Path. It means that 3.7 million people could escape poverty over the coming two decades via the interventions in the Infrastructure scenario.

The Governance scenario represents a reasonable but ambitious improvement in accountability and reduces corruption, and hence improves the quality of service delivery by government.

The intervention is explained here in the thematic part of the website.

As defined by the World Bank, government effectiveness ‘captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies’.

Chart 51 presents the impact of the interventions in the Governance scenario on government effectiveness.

East Africa’s average score of 1.7 on government effectiveness represents the average for sub-Saharan Africa. The region performs worse than Southern Africa with a score of 2.1, on par with West Africa and better than Central Africa with a score of 1.3 in 2019.

On the Current Path and in the Governance scenario, East Africa’s government effectiveness quality score is projected to improve to 2.18 or 2.31, respectively, by 2043. This means that the average for the region is expected to converge to the current government effectiveness performance of Kenya with a score of 2.3.

Country performances vary greatly across East Africa. Rwanda is the frontrunner with a score of 2.5 in 2019 while South Sudan and Somalia have scores of 0.2 and 0.4, respectively.

On the Current Path, GDP per capita is expected to increase to US$5 206 while the interventions in the Governance scenario have the potential to increase GDP per capita from US$2 320 in 2019 to US$5 364 in 2043.

In the Governance scenario, East Africa could reduce the share of the population living below the poverty line to 11.5% by 2043 compared to 12.2% on the Current Path. The interventions in the Governance scenario could prevent about 4.2 million people in East Africa from living in extreme poverty in 2043.

This section presents projections for carbon emissions in the Current Path for East Africa and the 11 scenarios. Note that IFs uses carbon equivalents rather than CO2 equivalents.

East Africa’s carbon emissions are projected to increase the most in the Combined Agenda 2063 scenario which combines all the sectoral scenarios. According to the Combined Agenda 2063 scenario, by 2043 East Africa is projected to emit 128 million tons of carbon, more than five times the 2019 level of emissions (23 million tons) and 24 million tons more than the Current Path forecast. The greater increase in the Combined Agenda 2063 scenario is the result of higher economic growth which leads to greater demand for energy. Among the sectoral interventions, it is the Free Trade and Stability scenarios that are expected to have the biggest impact on carbon emissions by 2043, resulting in additional emissions of 15 million and 4 million tons, respectively. The Leapfrogging scenario, on the other hand, will reduce carbon emissions to 93 million, 11 million tons below the Current Path forecast.