1. New evidence on Africa’s urban economy

Data on Africa’s cities and their economies is limited in comparison with most other parts of the world. As a consequence, Africa’s cities are often only perceived as overcrowded, congested and unproductive, and rapid urbanisation in in Africa is seen as a threat – or at best as a challenge that needs to be managed. This chapter disputes this perspective. It provides new evidence that urbanisation benefits Africa and has contributed to better economic outcomes and higher standards of living. While it is obvious that African cities face major challenges, the chapter shows that in most dimensions, they significantly outperform the rest of the country in which they are located. Often, the gap between the performance of African cities and the national averages is much larger than the corresponding gaps in many other parts of the world.

The chapter presents a large number of novel indicators on cities in Africa. The indicators have been derived from microdata sources containing millions of observations of individuals. Annex Table 1.A.1 provides an overview of the covered countries. They are constructed using the uniform definition of what is considered a city provided by the Africapolis database. As national definitions of cities vary, sometimes drastically, a uniform definition is a precondition for obtaining indicators that make it possible to compare countries. The data includes individuals who were surveyed regardless whether they work in the formal or informal economy. The indicators thus provide a representative average of the formal and informal economy, without distinguishing between the two. The chapter shows large and systematic gaps in the average performance of cities and rural areas and in the average performance cities of different sizes. Despite these clear patterns, it is important to keep in mind that Africa is a diverse continent with large variations in income levels and living standards. The averages presented in this chapter show the big picture, but they also hide the variation between cities of similar size. These differences cannot be explored in detail within the scope of a chapter, but they are nevertheless important.

The chapter expands on related work to develop quantitative assessments of cities in developing countries by using geocoded microdata, including Henderson, Nigmatulina and Kriticos (2019[3]), OECD/European Commission (2020[4]), and Gollin, Kirchberger and Lagakos (2021[5]). The data used in this chapter predate the COVID-19 pandemic. As of the time of writing, it was still unclear if the pandemic will have lasting effects on African cities (see also Box 1.4).

Jobs in African cities tend to require higher skills and are better paid than in rural areas. Infrastructure is better and services are more widely available. Urban residents receive a better education, and fertility rates and dependency ratios are lower than in rural areas. Underemployment is less prevalent, as urban workers work longer hours and are more likely to be in formal employment than rural workers. Larger cities tend to perform better than smaller cities in most outcomes. The share of firms that invest in research and development (R&D), for example, increases strongly with city size. However, the gap between large and small cities tends to be smaller than the gaps between rural areas and small cities.

Even though the urban population in Africa grew by approximately 500 million people between 1990 and 2020, African cities managed to preserve their good performance. In most measurable dimensions, the gaps between rural and urban areas remained largely stable over the period. Urbanisation has provided a strong boost to economic outcomes and living standards, allowing hundreds of millions of people to move out of economically lower-performing rural areas to benefit from better economic opportunities in cities. However, it is not clear that urban economies are transforming rapidly. For example, ownership rates of durable consumption goods, such as refrigerators, have remained stable or grown only slowly in the last three decades.

Importantly, benefits from urbanisation are also spreading to rural areas. In rural areas, proximity to cities is correlated with better outcomes. For example, the average education level or the share of skilled jobs in rural areas declines strongly with increasing distance from the closest city. As almost 5 000 new cities emerged in Africa between 1990 and 2020, millions of rural residents gained access to economic opportunities, services and infrastructure provided by urban areas.

While the chapter shows that African cities do well in the context of their countries, it is clear that they face challenges. Many African cities are not well planned, lack infrastructure and provide insufficient public services, compared to cities in other parts of the world. They face increasing levels of pollution and are threatened by climate change. The high costs of doing business reduce the competitiveness of their economies, and African cities are not experiencing the rapid transformation seen in cities in other emerging economies, such as China’s. African governments need to address these challenges if they want their cities to attain the levels of development attained in other parts of the world.

Despite the challenges facing African cities, the data makes clear that urbanisation in Africa provides vast economic and social benefits. Acknowledging this fact is a precondition for managing urban growth effectively. The challenges need to be addressed, but they are no argument for containing the urbanisation Africa is experiencing. Its rapid urbanisation is an opportunity that arises only once. Governments should focus their efforts on making the most of it.

Productivity levels of workers and firms in cities tend to be higher than in rural areas everywhere in the world, which is reflected in higher average wages for workers in urban areas. Africa is no exception to this pattern. Average hourly wages in the six countries for which wage data is available (ETH, MLI, MWI, NGA, TZA and UGA) are USD 0.51 in rural areas and USD 1.03 in medium-large and very large cities, a difference of approximately 100% (Figure 1.2). This wage gap is directly reflected in various measures of living standards, including consumption and wealth measures, but also in other outcomes, such as asset ownership, that are discussed below.

The gap in consumption levels between rural and urban areas is even larger than the gap in hourly wages (Figure 1.3), in particular the gap between small cities and rural areas. Several factors are responsible for this. As discussed below, hours worked are significantly higher in urban areas than in rural areas, which implies that the differences in total wages between rural and urban areas are larger than the differences in hourly wages. Moreover, dependency ratios are lower in urban areas than in rural areas. Per 100 residents of working age, there are roughly 30 fewer residents of non-working age in cities than in rural areas. This implies that a larger percentage of the population in cities is working than in rural areas, which increases average per capita income levels and hence consumption levels for a given wage level.1

Striking differences between rural and urban areas can also be seen in the wealth distribution. Whereas only 4% of the rural population belongs to the top wealth quintile of a country on average, the share is 30% in cities with between 10 000 and 50 000 inhabitants and increases to 59% in cities with more than 1 million inhabitants. Conversely, the share of residents in the poorest wealth quintile is 33% in rural areas but just 2% in the largest cities of more than 1 million inhabitants (Figure 1.4).3 This wealth gap is reflected in the ownership of specific assets. For example, 18% of residents in cities with more than 1 million inhabitants belong to a household that owns a car, while the corresponding figure in rural areas is just 3%.

As mentioned above, cross-country averages do not reflect the substantial variation between countries. For example, 18% of individuals in large cities live in a household that own a car, but this figure is not representative for many cities. In Cape Town (ZAF), 49% of all households own a car, whereas the corresponding figure is less than 3% in Kisii (KEN) (Figure 1.5).

Higher wages in cities do not translate into higher employment rates. Urban employment rates are in fact slightly lower than in rural areas. On average in cities, between 79% and 82% of men (aged 18-49) and 58% to 61% of women (aged 18-49) are in employment. In contrast, 85% of men and 60% of women are employed or self-employed in rural areas (Figure 1.6). Employment rates are generally higher for men than for women, but the differences in cities vary widely across countries. Figure 1.7 shows that North African cities in particular have very low employment rates for women. In other cities, such as Accra (Ghana), Antananarivo (Madagascar) and Lomé (Togo), employment rates for women are nearly as high as for men.

While employment rates in cities are slightly lower than in rural areas, this is more than outweighed by the difference in the number of hours worked. On average, employed rural residents work 36 hours a week, compared to up to 49 hours in cities with more than 1 million inhabitants – a difference of 39%. The high employment rate in rural areas masks significant underemployment, i.e. workers working fewer hours than they could if they had a choice, largely due to the seasonality of agricultural labour. Moreover, the share of salaried workers is significantly higher in larger cities.

Across the world, urban areas have more complex economies than rural areas. Cities in emerging economies are often centres of industrial activity, while cities in advanced economies tend to rely on services. In contrast, rural areas have a much stronger reliance on agriculture and extractive activity and often have lower shares of skilled and service sector jobs. Nevertheless, especially in high-income countries, agriculture tends to employ only a small fraction of individuals, even in rural areas. In the average OECD country, only 7% of all workers in rural areas are employed in agriculture (OECD, 2021[11]).

In Africa, agriculture is the dominant occupation in rural areas, at close to 60% of all workers employed. Urbanised countries tend to have somewhat lower rates of agricultural employment. In contrast, sales, skilled manual work, services, and professional, technical and managerial activities are the dominant occupations in cities of all sizes. Together, they make up approximately half of all jobs in urban areas (Figure 1.9). Even in small cities of less than 50 000 inhabitants, only 16% of the workforce are directly employed in agriculture. Of course, this figure does not take into account the indirect importance of agriculture, as a sizeable part of the workforce in these cities is employed in sectors that depend on agriculture, such as the trade or processing of agricultural goods.

The occupational categories provided by the DHS can be classified in skilled and unskilled occupations. While such a classification is only approximate, given the lack of detail within the available occupational categories, it shows a very clear pattern. The share of skilled occupations is significantly lower in rural areas than in cities. Among men, less than 20% of working individuals work in skilled occupations in rural areas, compared to approximately 50% in midsized and large cities. The share of women in skilled occupations is generally lower, but the pattern is the same: 11% of women are employed in skilled occupations in rural areas, while the share in cities of different sizes ranges from 20% to 25%. Thus, for both genders, the share of skilled workers in large cities is approximately 2.5 times that in rural areas.

Figure 1.11 shows the relationship between the share of skilled jobs and the urban/rural status by country. While country-level estimates necessarily have a higher sampling noise and are therefore less precise than aggregate numbers, it is notable that the pattern described above holds in most countries.

The higher share of skilled jobs in cities can – at least partly – be explained by the higher education levels. Whereas on average 42% of the population in rural areas in the covered countries does not have any formal education, the share is only 13% in large cities. Likewise, the number of average years of schooling varies from 4 years for women in rural areas to almost 10 years for men in large cities. Notably, the gender gap is somewhat smaller in large cities, indicating that urbanisation has benefits for female education. Importantly, these numbers refer to the entire population above age 18 and older and not to current graduates. As noted below, education levels in Africa are rising across rural and urban areas alike. Average education levels in the general population are thus lower than average education levels of young adults.

Cities attract educated people everywhere in the world. Thus, the difference in education levels could be due to the fact that more educated people tend to move to cities. However, the data do not support this hypothesis. Although rural-urban migrants aged 18-29 who move to cities at age 18 or older have on average 3 to 3.5 years more education than their rural peers of the same age who have never moved, they still tend to be less educated than urban residents aged 18-29 who grew up in a city. For all city-size classes, the education level of residents born there who have never moved is higher than the education level of rural-born residents who moved there at age 18 or older. The large gaps in education levels between cities and rural areas are thus not primarily due to selective migration, but to the easier access to education in cities and the greater importance of education in an urban economy.

The boost to education that urbanisation affords is arguably one of its most important benefits. Education has major positive influences on job opportunities, health outcomes and other dimensions of well-being over the course of a person’s lifetime (OECD, 2021[12]), meaning that the economic and social benefits will persist over many decades. This also implies that many of the economic benefits of the higher levels of education that children in African cities receive today will materialise only in the years to come.

Despite the benefits cities offer in providing access to education, it is important to emphasise that some cities do better than others. Figure 1.13 shows that large differences in education levels exist across cities of different sizes. Some cities are much more successful in providing secondary education to their residents and have shares of the population with secondary education that easily exceed 50%, while the share remains below 20% in other cities. As similarly diverse sets of outcomes can be found across many dimensions, it is a reminder that public policies matter and that the benefits of urbanisation do not materialise automatically.

Firms in larger cities are more likely to be engaged in innovative activities, such as creating new products or improvements (Figure 1.14). In small to medium-sized cities with fewer than 250 000 inhabitants, approximately 8% of firms develop new improvements to products, whereas the share is twice as high in cities with more than 1 million inhabitants. As innovation is the key driver of productivity growth, the increased innovative activity contributes to higher levels of productivity in larger cities.

Even though international evidence suggest that innovative firms export more (Bustos, 2011[13]) and that larger cities rely disproportionally on exports (Marin et al., 2020[14]), this pattern is not reflected in city-level data in Africa (Figure 1.14). Potentially, this is due to the outsized importance that the export of raw materials has in the export portfolio of African economies. As the producers of raw materials are likely to be located in rural areas and smaller cities, they counteract the export-enhancing effect of greater innovative activity in large cities.

Moreover, compared to cities with less than 250 000 inhabitants, firms in cities with between 250 000 and 1 million inhabitants have a share of employees with secondary education 7 percentage points higher and firms in cities of above 1 million inhabitants have a share 11 percentage points higher. This corresponds to the result noted earlier that the share of skilled jobs is higher in larger cities than in smaller ones.

Cities across the globe have higher levels of infrastructure than rural areas because infrastructure can be provided more efficiently to urban residents than to rural residents. Because of higher population densities, more people can benefit from a given infrastructure investment in a city than in a rural area. This makes it cheaper to provide infrastructure to city dwellers than to rural populations. Likewise, the provision of other essential infrastructure, such as transport and telecommunications infrastructure, is cheaper on a per capita basis in cities than in rural areas. High densities of people and firms in cities also allows the provision of infrastructure that would not be viable in rural areas, such as metro systems, airports and various forms of specialised industrial infrastructure, such as high-throughput data cables. Workers and firms become more productive because they benefit from this infrastructure.

In Africa, infrastructure levels between cities and rural areas differ substantially, which is reflected in the share of households that have access to electricity, piped water and telecommunications networks. The differences are most pronounced with respect to electricity provision. Less than 20% of households in rural areas have access to electricity, while the share reaches 58% in small cities with less than 50 000 inhabitants and 80% in cities with more than 1 million inhabitants. Access to piped water on the plot is generally much less widespread and varies from 7% in rural areas to 25% in small cities and 33% in large cities.4 In contrast, mobile phone coverage and ownership is widespread, with 63% of households owning a mobile phone in rural areas, 85% in small cities and 94% in large cities.

Notably, the gap between rural areas and cities (of any size) is much larger than the gap between small and large cities. The differences between rural areas and small cities in the share of households that have access to electricity, water and mobile phones, respectively, are approximately twice as large as the differences between small cities and large cities. This pattern holds for many other outcomes analysed in this chapter.

It is important to keep in mind that Figure 1.15 shows the general differences across rural areas and cities of different sizes, but it is not representative of individual countries. A breakdown of access to electricity by city-size class and country (Figure 1.16) shows that majorities of households in large and midsized cities have access to electricity. In contrast, electrification rates in small cities vary strongly across countries, while rural electrification rates are below 50% in most countries.

Access to piped water on the plot varies even more strongly across countries. Figure 1.17 shows the share of population with access to electricity and water for selected cities. Whereas electricity access is widespread in almost all large cities, access to piped water on the plot varies strongly across cities. While more than 80% of residents in Addis Ababa (Ethiopia) have piped water access on their plot, the share is just slightly above 10% in Accra (Ghana). Similar differences can be found across cities of all sizes.

The available data do not allow for measuring the scale of the formal and informal economy in rural and urban areas. However, urban areas are more likely to meet many of the preconditions to facilitate a transition to the formal economy. The share of individuals that own a bank account is 2 to 3 times higher in cities of different sizes than in rural areas. Likewise, close to half of all individuals own a title to their house in urban areas, while the share is less than 20% in rural areas. Urban residents are also more likely to have a birth certificate or to be registered with the public administration, even if the gap is small compared to the previous outcomes. While none of these factors alone is sufficient to enable a transition to the formal economy, the absence of any of them can create a bottleneck that prevents such a transition.

Mobile banking has made banking services available to large groups of people who did not previously have access to financial institutions. Figure 1.18 presents an average for the years 2010-2019, the share of residents in households with a bank account is probably an underestimation of today’s value. As Figure 1.19 shows, more than 80% of residents live in households that have a bank account in 2016 and 2018 in major African cities such as Lagos (NGA), Kampala (UGA) and Addis Ababa (ETH). Nevertheless, there are still large cities, such as Conakry (GIN), where less than a third of residents live in a household that has a bank account.

Africa has by far the highest dependency ratio of any global region. In 2015, on average, 100 working age people (aged 15-64) had to support 80 non-working age people. In contrast, dependency ratios in other global regions vary from 47 to 55 non-working age people per 100 working age people (UNDESA, 2019[17]). The high dependency ratio is mostly due to a high birth rate. More than 90% of the dependent population in Africa are children, whereas up to half of the dependent population in other parts of the world consists of the elderly.

Lower dependency ratios increase per capita GDP levels and improve living standards because the output produced by the working population has to be shared among a smaller non-working population. Assuming a constant employment rate and constant labour productivity, a decrease in the dependency ratio from 80 to the global average of approximately 50, would increase per capita GDP by 20%. Dependency ratios could decrease further, because high child dependency ratios carry the seeds of a demographic dividend. If fertility rates decline to replacement levels (approximately 2.1 births per woman), countries can quickly move from disproportionally high dependency ratios to disproportionally low dependency ratios, because the decline in fertility is quickly reflected in lower child dependency ratios, while it takes decades until it is reflected in higher old-age dependency ratios.

In the 32 countries for which data exists, total fertility rates in large cities averaged 3.7 births per women between 2010 and 2019, as opposed to 5.9 in rural areas (Figure 1.20 left panel). Across all of Africa, total fertility rates have declined from 6.2 in 1990 to 4.4 in 2020 (UNDESA, 2019[17]). This downward trend occurred relatively uniformly across rural areas and cities of any sizes, and the gap between rural areas and cities remained relatively constant over time. While the fertility rate is the most important determinant of dependency ratios, they are also affected by rural-urban migration. The large gap in dependency ratios between rural areas and small cities with between 10 000 and 50 000 inhabitants (Figure 1.20 left panel) that is not reflected in fertility rates could be an indication that rural-urban migrants into these cities do not take their children with them (perhaps because they are located close to their rural homes). However, further investigations would be necessary to confirm this hypothesis.

One of the most notable patterns in the data presented above is the good performance of small and midsized cities. Even small cities perform notably better than rural areas along almost all measurable outcomes. While they tend to perform worse than larger cities, the gap between small and large cities is usually much smaller than the gap between rural areas and small cities.

These results indicate that in many cases, certain minimum sizes or minimum population densities are required for an economic activity, a public service or an infrastructure provision. Once the required threshold is exceeded, any additional population does little to facilitate the economic activity or service or infrastructure provision further. For example, almost any city of 10 000 inhabitants will have enough students to allow for the efficient operation of a secondary school. While larger cities might allow for more specialised secondary schools or a larger choice of secondary schools, these gains tend to be small relative to the benefit that comes from exceeding the necessary population size that allows for the operation of a secondary school. Similar threshold effects might occur with respect to many other outcomes, such as the viability to have a specialised market or a bank branch in a city.

Of course, not all benefits of urbanisation occur at certain thresholds. Some benefits of urbanisation continue to accumulate with increasing city sizes and population densities. For example, larger cities tend to have more complex economies because they allow for more specialised economic activities. This complexity increases with city size, without depending on any obvious thresholds.

This chapter presents primarily averages by city-size class to show the typical patterns that can be found across Africa. Although such averages are meaningful and important, they only present a partial picture because they cannot reflect the substantial variation that exists across and within cities. As discussed above, large differences exist between cities, with many cities performing well in some dimensions, but not in others. Within cities, differences between poor and wealthy residents are even larger than average differences across cities. Due to the lack of suitable data, this report does not discuss the gap between the formal and informal sector. The statistics presented generally cover both sectors, even though economic and social conditions tend to be much more favourable in the formal than in the informal sector. Likewise, the place of residence within a city has major consequences on living standards. Slum dwellers often have much poorer access to services and infrastructure than individuals living in formal housing. Again, these differences have not been discussed in detail, given the lack of cross-country data that would permit a rigorous quantitative analysis.

Most cities generate significant agglomeration economies (OECD, 2015[18]). The term agglomeration economies describes a set of factors that increase productivity, wages and innovation when economic activity is located in close geographical proximity (Box 1.5). Cities, and especially large cities, are the main beneficiaries of agglomeration economies, because of the high density of their economic activity.

Africa’s cities have higher productivity levels than rural areas, and larger cities have higher productivity levels than smaller cities. This is reflected in higher wages (Figure 1.2) and higher GDP levels. While this pattern suggests that urbanisation contributes to better economic outcomes, it is not conclusive evidence. A possible alternative explanation could be that cities and especially large cities attract particularly productive workers or industries and advanced industries, a process that economists call sorting. If sorting were the only explanation for the good performance of cities, urbanisation would not benefit national economies, as the productive workers and industries that locate in cities would be equally productive if they remained in rural areas. Under this explanation, urbanisation would only affect where productive firms and workers are located, but it would not add to overall productivity levels across the national economy.

Sorting of workers and firms is common in most countries, including in Africa. As noted above, rural-urban migrants have on average 3 to 3.5 years more of education than rural residents of comparable age who do not move to cities. On average, an increase in city size by 10% is correlated with a 0.3 percentage point higher share of workers in skilled occupations. Figure 1.21 breaks down the relationship between city size and share of skilled job by country. For each country, it shows the estimated increase in the share of skilled jobs in response to a 10% increase in city size, as well as the 95% confidence interval for the estimate. For most countries, the estimated coefficient falls between 0.1 and 0.6, meaning that the share of skilled workers increases on average by 0.1 to 0.6 percentage points if the size of a city increases by 10%.

Yet, high levels of productivity in cities are not only due to sorting. Even when controlling for the education level of workers, their personal characteristics, such as age and sex, as well as the characteristics of the industries in which they work, workers in large cities are more productive than workers in small cities, who in turn are more productive than workers in rural areas (Annex Table 1.A.8 and Annex Table 1.A.7). Thus, the data suggest that cities in Africa generate agglomeration economies just as cities in other parts of the world do.

If the magnitude of agglomeration economies is known, it is possible to conduct back-of-the-envelope estimates of how urbanisation affects GDP. The key measure of agglomeration economies is the so-called city-size elasticity of productivity. This indicates by how much productivity increases if city size increases. To estimate the GDP effect of urbanisation in Africa, average city-size elasticities for five African countries (Ethiopia, Malawi, Nigeria, Tanzania, and Uganda) have been obtained (see Box 1.6). On average, labour productivity5 is estimated to increase by 0.3% if the urban population increases by 10% (see Specification 2 Annex Table 1.A.7). These numbers can be applied to the observed growth of urban and rural areas in order to get a first-order approximation of the consequences for GDP.

Across Africa, the current urbanisation process contributes 0.33 percentage points annually to per capita GDP growth, even without taking into account additional long-run and second-order benefits from urbanisation (e.g. due to the better education that children in cities receive). This is 29% of the total average annual per capita GDP growth across Africa between 2001 and 2020. Thus, even if second-round effects from higher education levels obtained by rural residents are not considered, urbanisation provides a significant contribution to Africa’s per capita GDP growth.

The effect of urbanisation on GDP can be divided into two components. First, the share of the population that lives in more productive urban areas instead of in less productive rural areas increases due to urbanisation. This shift in population distributions increases national GDP levels and hence average per capita GDP. Second, cities become more productive due to the inflow of additional workers, because larger cities have higher productivity levels than smaller ones. In terms of magnitude, the first component contributes approximately two-thirds, while the second component contributes approximately one-third to the predicted per capita GDP growth. The growing share of people living in more productive cities increases per capita GDP by 0.22 percentage points, while cities becoming more productive due to their larger size adds 0.11 percentage points.6

Several important caveats must be noted. On an econometric level, the estimated magnitude of agglomeration economies is subject to considerable uncertainty, due to the conceptual issues discussed in Box 1.6 and due to statistical noise. The city-size elasticity of productivity of 0.03 that is used for the back-of-the-envelope estimate is at the lower end of comparable estimates for developing countries (see Gover, Lall and Timmis (2021[20])). For comparison, if the city-size elasticity was twice as high (an estimate that would fall at the upper end of range of estimates in Gover, Lall and Timmis (2021[20]), the contribution of urbanisation to annual per capita GDP growth would be 0.56 percentage points, or 50% of total average annual per capita GDP growth between 2001 and 2020.

On a conceptual level, it is impossible to capture all consequences of urbanisation. The estimates provide only the GDP effect from productivity gains due to agglomeration economies. They do not take into account other consequences of urbanisation, for example the fact that it causes structural changes to the economy or that it changes the characteristics of the workforce (e.g. because children in urban areas receive more education than in rural areas). Moreover, the estimates only focus on productivity and do not take into account negative externalities from urban growth, such as increasing congestion or exposure to pollution. Thus, the estimates obviously do not capture the full impact of urbanisation, especially over longer time horizons.

Taken together, these caveats imply that the estimates above can at most provide a ballpark range of the medium-term economic gains from urbanisation. They should not be considered precise predictions, nor should they be seen as reflecting all the economic and social processes that are induced by urbanisation. It seems likely that the numbers above underestimate the long-term benefits of urbanisation, in particular in light of the significantly higher levels of education that urban residents receive compared to rural residents. These educational benefits will affect African economies for many decades, but their impact is hard to capture.

One of the most under-appreciated achievements of African cities over the last 30 years has been their consistently better performance compared to rural areas, despite absorbing large numbers of rural-urban migrants. In 1990, 3 300 cities with an average of 57 000 inhabitants existed in Africa. By 2015, those cities had grown to more than 140 000 inhabitants on average, and they probably attained an average population of 170 000 in 2020. In addition, another 4 900 cities with an average population of 22 000 inhabitants emerged during this period. Throughout this phase of extremely rapid population growth, cities managed to maintain their above-average performance. Since 1990, they have had a consistently higher share of skilled jobs, higher wealth levels and better infrastructure. If an indicator changed at the national level, the respective trends in rural and urban areas usually moved in parallel.

Figure 1.22 shows the evolution in the share of residents with electricity grid connection, piped water on the plot, the average years of schooling and the share of residents in households with a bank account. Three of the four outcomes show a clear upward trend across all territories, with larger cities mostly preserving their advantage over smaller cities and rural areas. An exception to the largely positive development is access to piped water. The share of residents in households that have access to piped water on their plot declined strongly in cities in the late 1990s and early 2000s. However, it has stabilised since then, and has remained constant, even as cities continued to grow strongly.

An implication of this stable pattern is that urbanisation benefited economic growth and improved living standards. Even if the relative performance of cities has remained constant, urbanisation has made a major contribution to an increase in living standards. Cities were able to absorb rural migrants as well as natural population growth, and to continue to perform well in their national context. As the urban population increased from less than 190 million people in 1990 to 570 million people in 2015 and approximately 700 million people in 2020, the capacity of cities to absorb this growth without measurable declines in living standards has allowed several hundred million people to work in better jobs, with improved access to services and infrastructure in cities. Absolute numbers provide another way to show the achievement of African cities. In the 30 years from 1990 to 2020, almost 390 million inhabitants were connected to the electricity grid.9 In 2020, about 270 million more urban residents live in households with a bank account than in 2000.

In rapidly growing countries, cities drive the modernisation of the economy. In countries such as China, urbanisation went hand in hand with a rapid expansion of the manufacturing sector in cities and a subsequent shift towards higher value-added economic activities. In Africa in recent years, despite the persistent good performance of cities compared to rural areas, cities do not appear to have led such a change.10

The percentage of skilled workers in different city-size classes has remained virtually constant (Figure 1.23).11 Although the share of some occupations that could be associated with a shift towards a more advanced economy has grown moderately (e.g. in professional/technical/managerial and service sector jobs), this growth has been distributed evenly, with no indication that cities have seen disproportionate growth (Annex Table 1.A.4).

Moreover, other data sources also point towards an absence of a significant modernisation of urban economies. Firm-level data shows that between 2010 and 2020, the share of firms engaging in R&D has declined by more than 10 percentage points. Over the same time, the share of firms engaged in export activity has remained approximately constant, even though the share of firms that follow international quality norms has declined by 5 percentage points.

Likewise, there is no indication of growing disparities in wealth or income that could be expected in a scenario in which cities lead an economic transformation. Emerging economies often experience a rise in income inequality as income levels rise, which starts to decline again as countries approach high-income status (following the well-known Kuznets curve). Rising inequality in emerging economies usually has a strong spatial dimension, as income levels in cities pull away from income levels in rural areas (see, for example, Yang (1999[26])). Yet, in Africa, no evidence of such a pattern exists. Changes in the wealth distribution have been minor.

Mirroring this trend, ownership rates of durable consumption goods have been largely stable in the past 30 years, in both cities and rural areas. For example, there has been virtually no change in average car ownership rates across cities of different sizes and rural areas (Figure 1.24).12 The share of households owning a refrigerator has grown by approximately 10 percentage points in cities as well as in rural areas, but remains below 50% even in large cities. Only the share of households owning a television has grown substantially, by 20 to 30 percentage points. This growth has been a national trend that affected cities and rural areas equally.

These trends suggest a nuanced conclusion. On the one hand, they clearly show the massive benefits of urbanisation in Africa, which are likely to continue. On the other hand, the absence of significant change in the urban economic structure indicates a persistent challenge for broader transformation. To ensure that cities drive lasting economic development, further policy measures are needed, as will be discussed in the subsequent chapters of this report.

Cities are essential for the functioning of rural economies. Depending on their size and proximity, different cities play different functions for rural areas. Cities serve as entry points to more connected and diversified economies for rural areas. They provide markets where agricultural producers can sell their products and rural households can access services and purchase basic necessities.13 In recent decades, the centre of gravity of the continent’s food system has shifted from rural areas to cities and towns. Today, cities and towns not only offer the greatest commercial opportunities for a region’s agricultural producers, but also act as nodes for food trade and markets. They are transport hubs that provide access to transport networks for goods and people. Intellectual and financial service providers, such as banks, accountants, lawyers and engineers, tend to be located in cities. Although rural businesses might need such services only infrequently, they are nevertheless indispensable for running a modern business. Often, they can be found in midsized cities, such as regional capitals, which can serve a large surrounding rural area.

Large cities offer specialised functions useful for some rural businesses. They serve as gateways to foreign markets, as they often host the local seats of multinational companies. They are usually the national financial centre, with major airports and ports. Usually, a country’s largest city is its capital and provides access to the government, which can be helpful for obtaining public tenders and influencing legislation. Large cities also tend to have the most specialised suppliers and to offer large markets of potential customers.

Being located close to cities facilitates businesses’ access to the facilities, services and market opportunities noted earlier. This reduces operating costs and offers opportunities for business development. Rural areas located close to cities can more easily be served by essential infrastructure that requires connections to larger networks, such as transport and electricity. These advantages are reflected in the structure of the economy in rural areas, which in turn influences the living standards of rural residents.

Africa’s rapid urbanisation is not only changing the urban landscape but also has profound effects on rural areas. Between 1990 and 2015, the number of cities in Africa more than doubled, from 3 319 to 7 721, with many cities emerging in rural areas with high population densities. As a direct result of the growing number of cities, more and more rural households live close to cities. Of rural residents, 50% live within 14 kilometres of a city, and 90% live within 47 kilometres. Fewer than 1.5% of rural residents are more than 100 kilometres from the closest city. Figure 1.25 shows the share of the rural population by distance to the closest city. The increasing proximity to urban areas allows a growing share of rural residents to access the services and amenities that cities offer.

Most rural households live closest to small and midsized cities. For more than two-thirds of rural residents, the closest city has between 10 000 and 50 000 inhabitants, while more than 20% of rural residents live closest to a city with between 50 000 and 250 000 inhabitants (Figure 1.25, right). In contrast, less than 10% of the rural population lives closest to a city of more than 250 000 inhabitants. These numbers underscore the importance of small and midsized cities, which are home to more than 250 million Africans (44% of the total urban population). They are also the closest urban centre for a large majority of rural households.

The benefits that rural areas receive from proximity to cities become clear if outcomes are plotted against distance to the closest city. Figure 1.26 shows the share of skilled workers in rural areas depending on distance to the closest city of at least 10 000 inhabitants. In rural areas just outside cities, the share is 22%, less than half the average of the share of skilled workers in cities, but still 4 percentage points above the average for rural areas. It declines steadily with increasing distance to the closest city, up to a distance of approximately 30 kilometres, where it stabilises at approximately 13%. The share of skilled jobs in rural areas close to cities is thus almost twice as high as the share of skilled jobs in remote rural areas.

Not shown on the figure are individuals who live more than 50 kilometres away from the closest city, less than 10% of all individuals in the data. At those distances, the relationship between distance to cities and outcome variables becomes unstable and begins to fluctuate. This can partly be explained by the fact that few people live so far away from cities. The number of observations in the data is thus low, which increases the statistical noise of the estimates. However, this may partly be an indication that remote rural areas often have distinct economies, which diverge from general trends.

Similar patterns can be found in a range of other outcomes. Figure 1.27 shows the relationship between distance to the closest city and outcomes such as average years of education, wealth levels, services and infrastructure access. Across all outcomes, there is a decreasing relationship with increasing distance to the nearest city, but the magnitude varies strongly from outcome to outcome. While the share of households with a bank account declines by a factor of three progressing from a distance of 1 kilometre to a distance of 50 kilometres, the relative decline in the share of residents with a mobile phone is much smaller.

Distance to the closest city (of any size) is by far the most important predictor of socio-economic condition. In addition, proximity to midsized cities of between 50 000 and 250 000 inhabitants, as well as proximity to large cities of above 1 million inhabitants, is correlated with better outcomes even when controlling for distance to the nearest cities. The benefits of distance to cities of different sizes are thus cumulative. When comparing two rural residents who live at a distance of 20 kilometres from a small city, but at 100  kilometres and 200  kilometres, respectively, to a large city of 1 million inhabitants, the resident closer to the large city is likely to be better off than the resident who lives farther away.

Significantly, the benefits of proximity to cities accrue regardless of the population density of rural areas. Although denser rural areas perform better in some dimensions, and rural areas that are close to cities tend to be more densely populated than remote rural areas, the benefits of proximity to cities persist even when controlling for population density in rural areas. This suggests that cities offer distinct benefits that do not emerge when the same number of people live close together in a rural setting (Annex Table 1.A.5 and Annex Table 1.A.6).

It is not only rural areas that benefit from proximity to cities. Small and midsized cities close to large cities also perform better than small and midsized cities far from large cities, even though statistically significant correlations can be found in fewer outcomes. Notably, average years of education are higher in small and midsized cities close to large cities, which is driven in particular by a higher share of residents with secondary or higher education. This could indicate that small and midsized cities benefit from better access to education offered in large cities nearby. By contrast, the performance of small cities does not depend on whether they are located close to midsized cities. Only proximity to large cities of above 1 million inhabitants is systematically correlated with better outcomes (Annex Table 1.A.6).


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← 1. It should be noted that wage and consumption data is calculated from Living Standard Measurement Survey data, which contains observations from only 30 000 individuals in six countries. The estimates are thus much less precise than the majority of the estimates in this chapter, which are based on more than 4 million observations collected by the Demographic and Health Survey from 32 countries.

← 2. This probability has been calculated by matching the built-up areas defined by Africapolis with the spatial population distribution according to the WorldPop population grid (Linard et al., 2012[28]; WorldPop[29]).

← 3. This statistic is based on a DHS wealth index classification of households into wealth quintiles. As some scholars criticise the DHS wealth index for underestimating the wealth of rural households, the figures potentially underestimate wealth levels in rural areas in comparison to urban areas.

← 4. See OECD (2021[27]) for a discussion of the governance challenges involved in providing water infrastructure.

← 5. Labour productivity is proxied by wages, as is common in the literature on the topic.

← 6. Per capita GDP in cities is predicted to increase by 0.22 percentage points. Because almost exactly half of Africa’s population lives in cities, this contributes 0.11 percentage points to overall per capita GDP growth.

← 7. Averages from 2000-2010 are used because they are more reliable than data for later periods. In the absence of censuses, recent population data is often based on projections and tends underestimate urbanisation.

← 8. This assumption implies that total factor productivity (TFP) growth due to agglomeration economies is identical to labour productivity growth and that the employment rate and capital stock remain unaffected by urbanisation.

← 9. In the early 1990s, 62% of the 190 million urban residents were connected to the electricity grid, but by the late 2010s, the share had risen to 72% of the 700 million urban residents.

← 10. In the early 2000s, 23% of the 290 million urban residents lived in households with bank accounts, while in the late 2010s, the share had risen to 48% of the 700 million urban residents.

← 11. Although it might appear that there are differential trends for the share of male skilled workers in cities of between 250 000 and 1 million inhabitants and cities of more than 1 million, Annex Table 1.A.4 shows that these trends are not significant (i.e. they are statistically indistinguishable from chance).

← 12. This does not conflict with the fact that car ownership levels at the national level have risen, as people moved from rural areas with low car ownership rates to cities with higher car ownership rates.

← 13. As countries develop, the importance of market towns as points of sale for agricultural producers declines, because of the emergence of wholesale traders that reduce the reliance on local markets. At the same time, market towns become more important as places to purchase goods, as living standards and disposable incomes of rural populations rise.

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