High Living Costs, Urban Agglomeration, and Inequality: Lessons from the Spatial Geo-Economic Politics of Urban Out-Migration in Sub-Saharan African Cities.

High Living Costs, Urban Agglomeration, and Inequality: Lessons from the Spatial Geo-Economic Politics of Urban Out-Migration in Sub-Saharan African Cities. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article High Living Costs, Urban Agglomeration, and Inequality: Lessons from the Spatial Geo-Economic Politics of Urban Out-Migration in Sub-Saharan African Cities. Etienne Nzabirinda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5902961/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper examines how high living costs, urban agglomeration, and inequality interact in Sub-Saharan African cities, focusing on the urban out-migration aspect of the spatial geo-economic politics involved. Using data from the World Bank and the GMM technique, the study identifies income inequality, rapid urbanization, and rising living costs as the main determinants of migration. The analysis reveals a net migration rate of 1.092 in large cities like Lagos, Kinshasa, and Johannesburg, driven by urban growth and economic opportunities that attract people. However, due to increasing living costs, high housing costs, and congestion, individuals are forced to move to more affordable and less crowded areas, resulting in out-migration. Income inequality, with a Gini coefficient of 0.81, where a few urban areas accumulate wealth while leaving a large portion of the population in poverty, is also linked to migration dynamics. Rapid urbanization, with an average growth rate of 4.403%, exacerbates spatial disparities and encourages migration towards peri-urban and secondary cities. The study also explores the influence of economic indicators on migration trends, using GDP per capita and GNI growth to show that economic growth and inequality play significant roles. Policy recommendations such as affordable housing, economic decentralization, slum upgrading, and improved access to services are suggested to address migration challenges and promote sustainable urban development. JEL Classification R23, O18, D31 Business and commerce/Finance Business and commerce/Operational research Scientific community and society/Geography Scientific community and society/Social sciences Social science/Economics Social science/Geography High Living Costs Urban Agglomeration Inequality Urban Out-Migration and Sub-Saharan Africa Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction In the last two decades, sub-Saharan Africa has seen a significant increase in its urban population, from 133 million in 1990 to over 360 million in 2022, making it one of the fastest urbanizing regions globally (United Nations, 2022). This demographic shift presents new economic opportunities and urban development prospects, but also brings about complex challenges, particularly for low and middle-income households. A major issue in cities is the high cost of living, which widens the gap between the wealthy and the poor, leading many to seek more affordable living arrangements outside of urban areas. This results in a shortage of housing and essential services, driving up property prices and rent. This exacerbates the housing crisis, forcing many to live in informal settlements. According to UN-Habitat (2020), over 58% of the urban population in sub-Saharan Africa resides in slums with inadequate infrastructure and living conditions. Cities like Lagos and Kinshasa struggle to provide affordable housing for their growing populations, leading to a reliance on informal housing (Cira, Kamunyori, & Babbar, 2016). Apart from housing, urban households in sub-Saharan Africa face higher costs for basic necessities like food and utilities compared to rural households. Studies show that urban households pay 20 to 30% more for essential goods due to inefficient logistics and underdeveloped infrastructure, impacting food security and financial stability (Lall, Henderson, & Venables, 2021; Frayne, Crush, & McLachlan, 2014). Urban agglomeration, the concentration of population and economic activities in specific urban centers, exacerbates inequalities. Wealthier households have better access to services like healthcare, education, and transportation, while poorer communities lack resources. Foreign investments and economic growth often favor affluent areas, widening socio-economic disparities (Satterthwaite & Tacoli, 2020). As a result, many low-income households move to peri-urban and rural areas to afford a decent living. However, these areas also face challenges like inadequate infrastructure and limited access to services, straining the local economy and accommodating the influx of migrants (Potts, 2020; Fox, 2021). This paper aims to examine the factors driving urban out-migration in sub-Saharan Africa, including high living costs, urban agglomeration, and inequality. It explores how these factors interact within a broader geo-economic and political context, and suggests governance reforms and investments in human capital as potential solutions to address these challenges. The paper is organized as follows: Section 2 presents the literature review, Section 3 describes the methodology, Section 4 sets out the analysis and results, and Section 5 concludes with policy recommendations. 2. Literature Review The following literature review synthesizes key research and theoretical frameworks relating to urbanization, high living costs, and economic disparities in Sub-Saharan Africa, situating the dynamics that drive urban out-migration and urban agglomeration in the region. 2.1 High Living Costs in SSA Cities The cost of living in most urban centers within SSA has increased, especially in major cities like Lagos, Nairobi, and Kinshasa. This increase is mainly attributed to housing shortages, inflation, and the concentration of wealth and economic opportunities in urban areas (UN-Habitat, 2020). Increases in housing costs are among the leading drivers of high living costs. For instance, house prices have increased up to 80% in just five years in some West African cities such as Abidjan and Douala, exacerbating the affordability crisis (UN-Habitat, 2023). According to the World Bank (2022), more than 60% of urban residents in SSA live in informal settlements with inadequate access to basic services and infrastructure. The informality of these settlements often leads to high density, poor sanitation, and low-quality housing, increasing economic vulnerability for low-income urban populations. 2.2 Urbanization and Economic Growth in Sub-Saharan Africa Rapid urbanization has characterized Sub-Saharan Africa, where a considerable population has migrated into urban centers in pursuit of better opportunities. By 2020, the urban population had reached 43%, and projections are that by 2050, this proportion will exceed 60% according to the United Nations high variant projection series, 2022. This is basically due to increased migration from rural areas for better job prospects, education, and healthcare. However, while urbanization in SSA is among the fastest-growing in the world (World Bank, 2022), it has also caused a growing gap between the rich and the poor. Economies have benefited unevenly from urbanization, and though the population may be growing in these cities, many residents face growing inequalities in incomes. 2.3 Economic Disparities and Migration Patterns This would be intrinsically related to rising economic disparities between urban and rural regions in SSA, while there is better remuneration and opportunities for jobs in urban centers, yet the cost of housing, transportation and basic services remain prohibitively high. For example, cities like Nairobi have a per capita GDP of approximately USD 2,400 and house costs alone take up as high as 70% of household incomes for many residents (World Bank, 2022). The rural areas are underdeveloped economically but have comparative advantages in terms of lower living costs, adding to their appeal among people unable to afford city lifestyles. This growing economic disparity often drives people back to rural areas or smaller, more affordable cities. According to UN-Habitat (2023), over 30% of the urban population in cities such as Mombasa and Kigali has migrated to smaller urban areas because larger cities have become unaffordable. This phenomenon, known as “urban displacement,” is becoming more pronounced in cities experiencing rapid population growth and high levels of economic inequality (Tacoli, 2017). 2.4 Urban Agglomeration and Informal Economies Urban agglomeration in SSA cities is characterized by the growth of large urban areas with high population densities, often without adequate planning or infrastructure. As cities expand, informal economies have become a defining feature of urban life. According to the International Labor Organization, 2021, it is estimated that more than 60% of the workforce in SSA cities are found in the informal sector, mostly in low-paid jobs that make them very susceptible to high living costs. This is exacerbated by the mushrooming of informal settlements in cities like Nairobi and Lagos, where basic services and infrastructure are lacking. Informal economies thrive in urban agglomerations where low-income migrants often settle on the periphery of the city to avoid high costs in city centers. However, these peripheries remain economically peripheral, exacerbating economic inequalities (Satterthwaite, 2016). 2.5 Agglomeration Economics Agglomeration economics are the economic efficiencies that result from the concentration of economic activities within cities. These advantages are due to the underlying two fundamentals: division of labor and economies of scale. Division of labor enables productivity increase due to specialization which in turn provides growth. The cities have the added advantages of this phenomenon of specialization in firms, industries, and functions that give rise to local advantages and further economic development (Duranton & Puga, 2004). External economies of scale, or agglomeration economies, further facilitate the process by reducing transaction costs such as transport and communication costs, while promoting knowledge spillovers in the process (Glaeser et al., 1992). These agglomeration economies are usually categorized into: 1. Localization Economies : Firms within the same industry benefit from being close to one another whereby labor, suppliers, and customers can easily share. 2. Urbanization Economies : Firms from different industries benefit from proximity to a variety of economic activities since the latter may enhance productivity and cause innovation. In the urban context, these economies make it possible to ensure better firms-labor matching, the spreading of the same infrastructures, and learning and innovation processes on the part of cities. Added to this, these can well be further causes determining the dynamism of local economies and their phases of specialization of services (Krugman, 1991; Marshall, 1890). 2.6 Evidence from Sub-Saharan Africa Although very few studies focus on agglomeration economics in the SSA region, studies that compare patterns of urbanization across regions suggest that in SSA this process is not always accompanied by economic outcomes similar to those in other developing regions. While urbanization is associated with economic growth and poverty reduction in other developing regions such as Latin America and Asia, that is not quite the case for SSA (Gollin et al., 2013; World Bank, 2015). Studies by Behrman et al. (2007) and the World Bank (2016) found that the levels of urbanization in SSA might not necessarily return the expected dividends with regard to reduced poverty levels and higher economic growth. This anomaly underscores the difficulty of realizing economic returns on urbanization in SSA, and perpetuates inequalities at the core of migration imperatives. 2.7 Sub-Saharan Africa Context Sub-Saharan Africa continues to be one of the fastest urbanizing parts of the world, with millions moving from rural areas into the urban centers in search of economic opportunities. However, high living costs in urban areas often push migrants into informal settlements where social networks and community support can help mitigate the challenges of relocation. Such is the argument by authors like Tacoli (2009) and Selod & Shilpi (2021). These informal settlements exacerbate inequalities, as they are often located in areas with insufficient infrastructure and lack access to essential services. This further increases the socio-economic divide between urban residents due to the failure of urban centers to provide reasonable housing and basic services, hence fueling migration pressures. People migrate circularly - that is, moving back and forth between rural and urban areas - searching for an equilibrium between higher living costs in urban areas with the relative affordability of rural areas (Serdeczny et al., 2017). Researchers such as Mueller et al. (2020) and Fields (1982) support that policies to be taken to reduce economic disparities, housing policy, and access to basic services will help alleviate pressures on out-migration in SSA. 3. Methods 3.1. Source of data Data for this study are extracted and combined from several databases, namely: The SWIID provides the Gini index to assess the level of income inequality across Sub-Saharan Africa. World Bank data on urbanization, industrialization, and GDP helps analyze economic growth and urban expansion. The UNDP offers urbanization rates, highlighting the rapid growth of cities in the region. Governance metrics provided by World Governance Indicators, crucial to the understanding of how effective policies affect living conditions. Finally, it uses the World Penn Tables Human Capital Index for education and labor market participation-both basic causes of migration and inequality. Table 1 Variable description, measurements, and data sources Variable Name Measure Data Source Income Inequality (-1) Gini index (lagged by 1 period) SWIID Urban Share Urban share (%) of population (total urban population ÷ total country’s population) World Bank Urban Share.Sq Square of the urban share (%) of population World Bank Urban Share > 1M Share (%) of urban population in agglomerations of more than 1 million people World Bank Urbanization Rate Ratio of urban population to rural population World Bank/UNDP Urbanization Rate.Sq Square of the urbanization rate World Bank/UNDP GDP per Capita Growth GDP Per Capita Growth (annual % change) World Bank GDP per Capita Growth.Sq Square of the GDP per Capita Growth (annual % change) World Bank Industrialization Share (%) of population employed in urban industries World Bank Governance Policy Preference Governance Effectiveness Index (proxy measure) WGI/World Bank Human Capital Index Human Capital Index per person (based on years of schooling and returns to education) World Penn Tables Source : Author (2024) According to sources of tables,1 data for this study was gathered from sources of international reliability. Gini index related to income inequality has been sourced from SWIID while urbanization, GDP Growth, Industrialization, and employment in urban industries' information is sourced out from World Bank, UNDP provides data related to Urbanization while Governance Effectiveness Index from WGI (World Governance Indicators) has been availed. Finally, human capital from educational attainment is captured by World Penn Tables. These data ensure consistency and credibility for analysis of economic factors determining urban out-migration in Sub-Saharan Africa. 3.2 Study setting 3.2.1 GDP per capita and urban agglomeration in Sub-Saharan African Cities. Figure 1 shows that the very low GDP per capita, averaging at $ 1,600 in Sub-Saharan Africa in 2022, contributes to rising income inequalities, especially in urban areas. Even with low incomes, rapid growth has marked cities like Lagos and Nairobi, with housing costs having increased by 50–80% in the past ten years. In Nairobi, for instance, despite the GDP per capita being $ 2,400, about 60% of the population lives in informal settlements that are characterized by inadequate infrastructure. High living costs and low incomes make urban life unaffordable for many, who then move to city fringes or rural areas where living is cheap but economic opportunities are limited. Although cities like Lagos have higher GDPs, the growth is concentrated in certain sectors, leaving the majority of the population behind and contributing to continued rural population growth, although at a slower rate of 1.2% annually. 3.2.2: Employment in agriculture vs. urban population, 1991–2024. From Fig. 2 , between 1991 and 2024, Sub-Saharan Africa experienced a shift from agriculture to urbanization. Agricultural employment decreased from 65–45%, while urban populations grew from 25–30% to over 50%. This migration to urban areas has created economic opportunities but also exacerbated inequality, as many people work in informal sectors. The decline in agricultural jobs raises concerns about food security, as urban areas become more reliant on food imports, while rural regions face labor shortages. This trend is evident in cities such as Kinshasa (DRC), Lagos (Nigeria), Johannesburg (South Africa), Nairobi (Kenya), Luanda (Angola), Dar es Salaam (Tanzania), Accra (Ghana), Abidjan (Ivory Coast), Douala (Cameroon), and Kampala (Uganda) 3.2.3 Descriptive statistics and stylized facts Based on Table 3 , in Sub-Saharan African cities, high living costs are influenced by a range of factors including urbanization, income inequality, industrialization, and environmental conditions. For instance, Abidjan and Accra, with high urbanization rates of 61.17% and 56.58% respectively, are experiencing increased living costs due to overcrowding, limited affordable housing, and strained infrastructure. Apart from these challenges, they also have to address significant income disparities: Abidjan with a Gini coefficient of 40.60 and Accra with a Gini coefficient of 42.10, characterized by high wealth disproportions that continue to push poor people to slums, increasing vulnerability to price increases. Like Abuja with 56.25% and Lagos with 60.34% also face high living costs due to fast-growing urbanization and higher income disparities, especially Lagos with a Gini coefficient of 50.31. As these cities expand, economic opportunities become limited, leading to overcrowding and price inflation in everyday goods. In contrast, Durban and Johannesburg in South Africa are largely industrialized, with Johannesburg having a higher industrial share of 23.44%. The industrial absorption may have partly eased the pressure on living costs by creating job opportunities for residents. However, income inequality, with a Gini coefficient of 0.63 for Johannesburg, still causes disparities in wealth distribution and fuels migration into less urbanized areas with lower costs. Luanda, with a high urbanization rate of 50.77% and a Gini coefficient of 1.00, experiences very high living costs despite relatively modest economic growth of a 1.02% GDP per capita increase. Industrialization in Luanda and cities like Lusaka, which has an industrialization rate of 11.99%, tends to raise living costs due to environmental consequences such as high carbon emissions, especially in Luanda, which emits 28,330 kt of CO2, further escalating living expenses. These high costs push many residents to seek affordable living conditions in rural or peri-urban areas, resulting in urban out-migration. On the other hand, Mogadishu, Kampala, and Dodoma have relatively better climatic conditions that lower the cost of living, reflected in relatively lower food prices and increased food security due to improved agricultural productivity. However, they have relatively weaker agglomeration with urbanization rates of 39.35% and 42.87% compared to Kampala and Dodoma, reducing the pressure associated with living costs compared to cities like Kinshasa (79.11%) and Dar es Salaam (74.78%), which show high overcrowding. Nairobi, with a high level of industrialization and an urbanization rate of 66.11%, faces high demand for services and infrastructure that raise the cost of living, despite a diversified economy. Its growth rate, driven by industrialization, has not yet managed to alleviate inequality in the pressure it exerts on housing and basic services, forcing residents to move to rural areas in search of affordable living. Cities like Windhoek, Pretoria, and Cape Town, with higher levels of industrialization and relatively stable economic growth, offset some of the pressures on the cost of living. Even in those cities, however, youth unemployment acts as a major driver for migration. For example, Pretoria and Johannesburg have very high rates of youth unemployment among the growing population, forcing them to seek better opportunities elsewhere. Apart from Lusaka and Harare, where a fair proportion of their populations live in slums or informal settings, driving up living costs due to inadequate infrastructure and unequal access to services, Juba and Maputo report higher costs driven by rapid urbanization without suitable infrastructure or resources available to serve the expanded population, resulting in accentuated inequity. 3.2.4 Study of geographic location of largest agglomeration city in sub-Saharan Africa From Fig. 4 , cities such as Kinshasa, Lagos, Johannesburg, Nairobi, Luanda, Dar es Salaam, Accra, Abidjan, Douala, and Kampala illustrate the complex challenges of rapid urbanization in Sub-Saharan Africa. As these cities grow, living costs rise sharply due to increased demand for housing, goods, and services. For instance, in Lagos and Kinshasa, housing prices are soaring, forcing many to live in informal settlements with limited infrastructure. Urban agglomeration creates economic opportunities but also strains resources, with cities like Nairobi and Accra becoming hubs of commerce while grappling with overcrowded streets and underdeveloped public services. Inequality remains a key issue, with wealth concentrated in specific areas while large portions of the population work in informal sectors with low wages and poor conditions, as seen in cities like Abidjan and Douala. 3.3 Econometric Model A dynamic econometric model estimated using GMM will be employed to analyze high living costs, urban agglomeration, inequality, and urban out-migration in Sub-Saharan African cities. Following the stylized UE model of Bertinelli and Black (2004), we take a step further with an empirically testable prediction: the quality of urban infrastructure significantly influences economic performance by enhancing the benefits of urban agglomeration. Equations (1) are rewritten as follows: ΔY(e,t)=α + βlog(Y(e,0))+γ1HHI(e,0)+γ2UI(e,0)+∑n = 1NδnX(e,t)n+ϵ(e,t) (1) where UI(e,0) represents the urban-specific quality of urban infrastructure. Regression analysis across nations, based on Eq. (1), is conducted using pooled OLS. The main focus is on the panel dynamic model regression estimated using system GMM suggested by Arellano and Bover (1995). Eq. (2) is rewritten in dynamic panel format: ΔY(e,t) = α + βlog(Y(e,t-1)) + γ1HHI(e,t-1) + γ2UI(e,t-1) + ρ∑n = 1NδnX(e,t)n + µe + τt + ϵ(e,t) ( 2) Here, t denotes the five-year interval period, and µe, τt, and ϵ(e,t) are error terms. Eq. (3) is estimated in a simple AR(1) specification that includes the interaction term of urban agglomeration and urban infrastructure: Y(e,t) = α + βlog(Y(e,t-1)) + γ1HHI(e,t-1) + γ2UI(e,t-1) + γ3(HHI(e,t-1)*UI(e,t-1)) +ρ∑n = 1NδnX(e,t)n + µe + τt + ϵ(e,t) (3 ) The interaction of urban agglomeration and urban infrastructure is presented by multiplying the two proxies, i.e., HHI(e,t-1)*UI(e,t-1). A country-specific effect denoted by τt captures time-invariant determinants of GDP per capita, which may or may not be correlated with urban agglomeration, motivating the panel regression estimation. To identify the drivers of urban out-migration in Sub-Saharan Africa by analyzing how high living costs, urban agglomeration, and income inequality influence internal migration. The Dynamic Panel Data Model will be applied to describe how changes in these factors affect migration across cities in Sub-Saharan Africa over time. The econometric model used for estimation is specified as follows: Migration it = α + β1Income inequalityit-1 + β2Urbanization Rate it + β3Night-Time Lights it + β4GDP per capita it + β5Slum%it + β6Youth Unemployment it + β7Food insecurity it + β8Agglomeration it + β9Health expenditure it + ϵit (4) urban migration in Sub-Saharan Africa is largely driven by a combination of high living costs, urban agglomeration, and economic inequality. As cities grow, rising costs and overcrowding push many people, particularly from poorer backgrounds, to migrate to less populated areas or rural regions. Income inequality within cities further exacerbates these migration patterns, with poorer individuals often unable to afford basic services or housing in urban centers. Youth unemployment and food insecurity also play significant roles in driving people to seek better opportunities elsewhere. In some cases, economic growth in cities may create new opportunities but also increase living costs, making migration a complex balancing act between seeking jobs and dealing with urban pressures. 4. Results 4.1 Descriptive Statistics of data This section presents descriptive statistics and stylized facts based on an analysis of urban growth, inequality, and migration trends in Sub-Saharan Africa. The data highlights the factors influencing whether high living costs and urban agglomeration push individuals to migrate out of cities. By examining these variables, the analysis identifies the patterns that suggest a connection between rising urban living costs, the concentration of economic activities, and the increasing disparities within urban populations, which may compel people to leave for more affordable and less congested areas. Table 2 shows urban, economic, social, and environmental trends in Sub-Saharan cities. Variable N Mean SD Min Max Log Net migration 400 1.092 0.23 0.8 1.9 Log Income inequality 250 0.81 0.9 0.43 0.73 Urban rate (% of total population) 320 4.403 1.545 1.021 9.237 Log Urban rate (past five years) (%) 320 5.287 0.589 3.08 9.012 Log Night-time lights 100 6.40 1.320 3.75 10.90 Log GDP per capita (log) 220 7.45 0.840 5.60 9.45 Log GNI (annual growth, %) 210 1.25 0.980 1.230 2.210 Log Employment in industry (%) 200 2.595 0.483 2.90 3.485 Log Slums (% of urban population) 150 0.310 1.187 0.01 1.70 Log Agglomeration 160 4.650 0.410 3.50 4.700 Log Employment in agriculture (%) 190 7.510 0.185 6.00 8.8 Log Health expenditure (% of GDP) 200 1.89 1.210 0.40 4.75 Log Mobile cellular subscriptions 210 2.530 0.324 1.175 2.6770 Log Current education expenditure (%) 130 5.020 1.230 7.790 9.890 Log Agricultural land (%) 210 4.835 1.875 4.20 8.500 Log Forest area (% of land area) 200 2.485 1.090 0.10 9.050 Log Youth unemployment (%) 220 1.690 1.430 2.20 5.001 Log Food insecurity 160 4.990 1.650 2.050 8.530 Table 2 provides descriptive statistics of Sub-Saharan African cities, offering critical insights into the nature of urban out-migration. This phenomenon is characterized by high living costs, agglomeration, and inequality. The net migration rate averages 1.092, indicating a significant inflow into urban centers, particularly in cities like Lagos, Kinshasa, and Johannesburg, which serve as economic hubs despite facing challenges such as housing shortages. In contrast, migration rates are low for cities like Mogadishu and Juba due to political turmoil. Income inequality is high, with a Gini coefficient of 0.81, leading to a concentration of wealth in cities such as Johannesburg and Lagos. A large proportion of residents in these cities experience poverty and lack access to services, motivating migration towards cities with more economic opportunities. The average urbanization rate is 4.403, with high growth rates in cities like Abuja, Accra, and Kampala putting pressure on housing and infrastructure. In contrast, cities like Windhoek and Dodoma grow at a slower rate, allowing for more manageable expansion. Over the last five years, the average urban growth rate has been 5.287, resulting in intense sprawl in cities like Lagos, Nairobi, and Johannesburg, straining resources and services. Nighttime light intensity averages 6.40, indicating high demand and economic activity in cities like Lagos and Kinshasa, leading to increased living costs. Cities such as Abuja, Johannesburg, and Cape Town have a higher GDP per capita at 7.45, though this wealth is still unequally distributed. GNI growth is at 1.25, showing steady economic expansion in cities like Accra, with slower growth in Kinshasa and Mogadishu limiting job creation and prompting migration. Industrial employment remains low, with more opportunities in Cape Town and Mogadishu needing to be created. The average slum population is 0.310, highlighting urban housing crises that intensify migration due to inadequate living standards. Agglomeration, averaging 4.650, reflects economic processes that ensure growth while increasing living cost inequality. Youth unemployment averages 1.690, posing a significant challenge in cities like Kinshasa, Lagos, and Mogadishu, leading to increased migration in search of better job prospects. Food insecurity, averaging 4.990, also contributes to migration, with cities like Kinshasa and Lagos struggling to provide affordable and nutritious food to their populations. 4.2 Linear GMM Estimation results Table 3 Linear GMM Estimation results highlight the interplay between urban dynamics, economic geography, and inequality, specifically focusing on the drivers of urban out-migration in Sub-Saharan African cities. Variables Coefficient Z-value P-value > Z Constant −0.0012 0.432 0.005 Log Migration 0.1823* 2.253 0.024 Log Income Inequality (Lag) 0.9784*** 75.262 0.000 Log Urban Rate (% of Total Pop) −1.5023 −1.056 0.001 Log Urban Rate (Past 5 Years, %) −0.0034 −0.162 0.001 Log Night-Time Lights (Log) 0.7356* 1.526 0.007 Log GDP per Capita (Log) −0.0163 −0.429 0.008 Log GNI (Annual Growth, %) 0.2418*** 3.358 0.001 Log Employment in Industry (%) 0.0023 0.841 0.000 Log Slums (% of Urban Population) 0.0029*** 4.821 0.000 Log Agglomeration −0.2041 −1.293 0.006 Log Employment in Agriculture (%) 0.1124*** 4.887 0.000 Log Health Expenditure (% of GDP) −0.0754 −1.794 0.043 Log Mobile Cellular Subscriptions 0.0034 0.340 0.004 Log Agricultural Land (%) −0.0342 −1.587 0.113 Log Forest Area (% of Land Area) 0.0587 1.634 0.002 Log Youth Unemployment (%) 0.1854** 2.438 0.015 Log Food Insecurity −0.0131** −2.620 0.009 Notes: Significance Levels: *p < 0.01, p < 0.05 , p < 0.1 . Results from Linear GMM Estimation in Table 3 imply that the high cost of living, agglomeration, and inequality are key drivers of out-migration in urban dynamics for cities like Abidjan, Lagos, Nairobi, and Johannesburg. In Lagos, rapid urbanization results in informal settlements, housing shortages, and high rents, leading low-income residents to move to peri-urban areas or secondary cities like Ibadan. Similarly, in Nairobi, slum conditions and high food prices drive many inhabitants to migrate, as indicated by the coefficient for Log Slums of 0.0029. Urban agglomeration presents challenges in cities like Johannesburg and Dar es Salaam. While Johannesburg's economic opportunities attract migrants, congestion, housing shortages, and inequality push others to nearby areas like Pretoria. Dar es Salaam faces similar issues with poor urban planning, leading to outward migration to regions like Dodoma. The negative coefficient for agglomeration (Log Agglomeration, coefficient: -0.2041) reflects this trend. The positive relationship of night-time lights (Log Night-Time Lights, coefficient: 0.7356) in cities like Nairobi and Cape Town shows how developed urban areas attract migration and contribute to population growth. Inequality is a significant issue in cities like Kinshasa and Luanda, where income disparities force marginalized groups to migrate. The positive and significant coefficient for income inequality (Log Income Inequality: 0.9784) highlights wealth concentration in urban centers. In cities like Harare and Mogadishu, high youth unemployment (Log Youth Unemployment: 0.1854) further drives migration trends. To address out-migration, targeted urban policies tailored to each city's challenges are necessary. A affordable housing programs and improved infrastructure can alleviate living costs in cities like Addis Ababa and Kampala. Cities like Lusaka, Juba, and Nairobi could benefit from policies promoting equitable economic growth, education, and youth job creation. Addressing these challenges will make Sub-Saharan African cities more inclusive and sustainable. The estimates from Table 3 have important implications for Nairobi, where high living costs, agglomeration, and inequality drive out-migration. As the capital of Rwanda, Nairobi experiences rapid urbanization and high housing demand. High living costs push low-income residents to the outskirts, reflecting the impact of slum populations and housing affordability (Log Slums, coefficient: 0.0029). Despite infrastructure improvements, congestion and unequal resource distribution contribute to migration patterns (Log Agglomeration, coefficient: -0.2041). Income inequality is a major driver of urban migration in Nairobi, with wealth concentration limiting opportunities for low-income groups. The high coefficient for income inequality (Log Income Inequality: 0.9784) forces migration in search of better living standards. High youth unemployment (Log Youth Unemployment: 0.1854) further exacerbates migration trends. Policies in Nairobi should focus on reducing income inequality, providing affordable housing, and creating job opportunities for youth. Rural-urban integration, improved public services, and sustainable urban growth strategies are essential to address these challenges. 4.3 Non-linear estimates GMM Estimations Table 4 Non-linear estimates GMM Estimations results highlight the interplay between urban dynamics, economic geography, and inequality, specifically focusing on the drivers of urban out-migration in Sub-Saharan African cities Variables Coefficient Z-value P-value > Z Constant 45.231*** 4.621 0.000 Log Income Inequality 0.432** 2.127 0.033 Log Urban Rate (%) −1.231** −2.398 0.017 Log Urban Rate (%)² 0.032* 1.884 0.060 Log GDP per Capita (Log) −3.214*** −3.514 0.001 Log GDP per Capita (Log)² 0.543** 2.218 0.027 Log Employment in Agriculture (%) 0.763*** 3.528 0.000 Log Slums (% of Urban Population) 0.419** 2.442 0.015 Log Employment in Agriculture × Slums −0.032* −1.896 0.058 Hansen’s J-statistic (Over-ID Test) 12.643 - 0.183 AR(1) −2.541 - 0.011 AR(2) −1.231 - 0.218 Observations (N) 320 - - Notes: Significance Levels: *p < 0.01, p < 0.05, p < 0.1 . Table 4 : Non-linear GMM estimates provide significant insights into the spatial geo-economic dynamics driving urban out-migration in Sub-Saharan African cities such as Abidjan, Lagos, Nairobi, Johannesburg, and Kigali. High living costs, urban agglomeration, and inequality are all intertwined within the rapid urbanization-economic inequality matrix, influencing migration patterns. In cities like Lagos, Kinshasa, and Abidjan, slum populations (Log Slums, coefficient: 0.419) are among the most significant drivers of migration. Slums often have poor housing conditions, high living costs, and a lack of basic services that push residents to seek better opportunities outside metropolitan areas. For example, large informal settlements and high cost of living are major drivers of migration from Kinshasa. This interaction implies that Log Employment in Agriculture and Slums is less effective in absorbing migrants when urban poverty or slum conditions complicate rural-urban migration flows in cities like Dar es Salaam or Lusaka (coefficient: -0.032). The findings also elucidate the complex dynamics within the relationship between urbanization and migration. In cities like Nairobi and Johannesburg, where urbanization rates are high, initial urban growth tends to be associated with better economic opportunities that decrease the likelihood of migration (Log Urban Rate, coefficient: -1.231). In cities like Cape Town and Kigali, the positive non-linear effect of urbanization (Log Urban Rate (%), coefficient: 0.032) indicates that increased population density and resource strain may eventually increase migration pressures in those cities. For instance, Cape Town is experiencing rapid growth, particularly in informal settlements, and congestion in certain areas may drive residents to move to less congested areas. Another underlying cause of migration patterns is economic factors, especially GDP per capita. In cities like Pretoria and Durban, high GDP per capita (Log GDP per Capita, coefficient: -3.214) is typically associated with higher economic opportunities and lower out-migration. Conversely, in cities like Luanda and Abuja, rapid economic growth may lead to increasing inequalities; the Log GDP per Capita² term (0.543) suggests that above a certain threshold of economic growth, migration pressures may rise due to increasing inequalities and overcrowding. Income inequality is also a significant push factor for migration across these cities. The coefficient for Log Income Inequality (0.432) indicates that urban centers with high inequality between the rich and the poor, such as Harare and Maputo, are prompting low-income earners to migrate in search of a more favorable living and economic environment. Cities like Addis Ababa and Mogadishu, despite being economic hubs, experience significant out-migration due to inequalities and lack of access to basic facilities. Rural employment also plays a considerable role. In cities like Juba, Dodoma, and Lusaka, rural employment opportunities, particularly in agriculture, serve as an attractive option for migrants from urban centers. The positive coefficient for Log Employment in Agriculture (0.763) demonstrates that agriculture remains a significant source of livelihood for many, especially in countries where the rural-urban divide is prominent. However, the interaction between agricultural employment and slum conditions suggests that slum-dwelling populations in cities like Kampala and Johannesburg may struggle to transition into agricultural work due to lack of resources or support for such a shift. 4.4 Robustness Check The robustness of the results was assessed through Hansen's J-statistic test. First, alternative model specifications, including different lag structures and regional controls, confirmed that the key relationships between high living costs, urban agglomeration, and inequality remained consistent. Expanding the sample to include more cities like Kinshasa and Luanda did not alter the findings, indicating that the results are generalizable across different urban areas in SSA. Instrument validity was confirmed through Hansen’s J-statistic and serial correlation tests, ensuring no endogeneity issues. Placebo tests also showed no significant effects, further validating the results. Non-linear relationships, particularly for GDP per capita and urbanization, were stable across different specifications. Sensitivity analyses with various data sources and time periods confirmed the robustness of the findings, supporting the conclusion that high living costs, inequality, and urbanization are key drivers of migration in Sub-Saharan African cities. 4.5 Hansen’s J-statistic Statistic Value P-value Hansen's J-statistic 12.643 0.183 The p-value of 0.183 follows Hansen's J-statistic for the Two-Stage Least Squares estimation model presented in Table 5. This reveals the validity of the instruments taken up by the model-that is, the consumption-investment instrumental variable does not necessarily correlate with the error term, meaning there isn't over-identification in this model. This means that the model can actually analyze the drivers of urban out-migration in some cities across Sub-Saharan Africa, including Abidjan, Abuja, Accra, Addis Ababa, Cape Town, and many others. 5. Concluding remarks This paper examines the high living costs, urban agglomeration, and inequality in cities of Sub-Saharan Africa using the spatial geo-economic politics of urban out-migration. Using the GMM technique with data from the World Bank, this study identified key drivers of migration and pointed to linkages between income inequality, rates of urbanization, and the cost of living. A very significant movement across the studied cities is marked by an average net migration rate of 1.092, whereas the presence of mega economic hubs like Lagos, Kinshasa, and Johannesburg on one side promotes immigration. However, simultaneously cities are afflicted by problems like a high cost of living and inadequate housing that make people move to cheaper and less congested areas, thus facilitating urban out-migration. The main determinants of migration are income inequality, with a Gini coefficient of 0.81. Cities like Johannesburg and Lagos demonstrate high concentrations of wealth in few areas and leave the rest of the population to reside in areas mostly without access to key services. This situation fuels migration as lower-income residents move to areas with more opportunity. Other cities like Abuja, Accra, and Kampala are growing at an average growth rate of 4.403% per year, thus contributing to the sprawl condition of these cities. For example, cities like Windhoek and Dodoma are growing at a relatively slower pace, which could ease pressure on infrastructure. The rapid growth of cities has been attributed to migration into the peri-urban or secondary cities in search of housing affordability and less congestion. This is further reflected in the GDP per capita, averaging 7.45, showing that there is growing wealth hand in hand with considerable poverty. A GNI growth rate of 1.25% indicates steady expansion in cities like Accra, though others, like Kinshasa and Mogadishu, are slow and cannot create many jobs, thus contributing to migration. The employment rate in industries is low at an average of 2.595%, while economic mobility within the cities remains restricted, especially in Cape Town, where higher economic output has been recorded. Slums themselves take a share of 0.310% of the urban population and are normally associated with housing crises, which in turn spur migration to other less costly places to live in, such as Lagos and Nairobi. Agglomeration, with a mean score of 4.650, is a symptom of the concentration of economic activities in large cities, exacerbating living costs and social inequality. In cities like Johannesburg, the pull of opportunities attracts migrants, while housing shortages and congestion push others to seek less crowded areas. Youth unemployment, averaging 1.690%, is another significant factor driving migration, particularly in cities like Kinshasa, Lagos, and Mogadishu. Food insecurity, averaging 4.990, further exacerbates migration pressures, with cities like Kinshasa and Lagos struggling to provide affordable, nutritious food. Linear GMM estimation results highlight the significant impact of urban dynamics on migration. The positive coefficient for income inequality (0.9784) suggests that increasing inequality intensifies migration pressures. Similarly, the coefficient for slums (0.0029) indicates that slum prevalence contributes to higher migration rates in cities like Nairobi. The economic indicators of GDP per capita and GNI growth rate demonstrate the migrants' attraction to cities with higher economic output, such as Cape Town and Abuja, but internal inequalities often compel out-migration. These patterns are furthered in the results from nonlinear GMM estimation: the relationship of slums to migration is positive, with a coefficient of 0.419, indicating that slum populations remain the key driver of migration into cities like Kinshasa and Abidjan. This is the complex relationship that may exist between urban growth and migration, with rapid urbanization creating economic opportunities but also eventually leading to overcrowding and inequality that may force residents to look elsewhere for opportunities. Entailing this very dynamic of initial growth fostering better opportunities but once the threshold reached resource strain increases migration pressures, the coefficient for urban growth stands at − 1.231 while 0.032 is maintained for its squared term. Economic factors are also very important, as shown by the negative relationship between high GDP per capita (− 3.214) in cities such as Pretoria and Durban, and out-migration. High economic output usually means more opportunities and lower migration rates; however, in cities that experience increasing inequality, such as Luanda and Abuja, migration pressures rise due to overcrowding and limited resource availability. Income inequality remains a strong push factor, with high levels of out-migration recorded in cities like Harare and Maputo due to the widening gap in wealth. Addressing urban out-migration in Sub-Saharan Africa requires policies oriented toward affordable housing, economic decentralization, and upgrading slums that reduce the cost of living and inequality. Similarly, programs dealing with youth employment and access to services such as education and healthcare are crucial. The development of infrastructure and the promotion of urban agriculture will go a long way toward improving rural-urban linkages and reducing migration pressures. Ultimately, sustainable urban growth with good governance ensures the implementation of such policies to achieve inclusive and resilient cities. Declarations Author Contribution Nzabirinda Etienne led the study design, was responsible for drafting and revising the manuscript Acknowledgements We are grateful to the World Bank for making the data accessible through its portal. We also thank the anonymous reviewers for their comments that have significantly enhanced this paper. References Adams, S., & Klobodu, E. K. M. (2019). Urbanisation, economic structure, political regime, and income inequality. Social Indicators Research, 142 (3), 971–995. Ahrend, R., Lembcke, A. C., & Schumann, A. (2017). The role of urban agglomerations for economic and productivity growth. International Productivity Monitor, 32 (3), 161–179. Ali, I. M. A., Attiaoui, I., Khalfaoui, R., & Tiwari, A. K. (2021). The impact of urbanization and industrialization on income inequality: Evidence from method of moments quantile regression. Social Indicators Research, 161 (1), 29–50. Altunbaş, Y., & Thornton, J. (2019). The impact of financial development on income inequality: A quantile regression approach. Economics Letters, 175 (3), 51–56. Alvarez, J., & Arellano, M. (2022). Robust likelihood estimation of dynamic panel data models. Journal of Econometrics, 226 (1), 21–61. Anand, S., & Kanbur, S. R. (1993). The Kuznets process and the inequality—development relationship. Journal of Development Economics, 40 (1), 25–52. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58 (2), 277–297. Arouri, M., Youssef, A. B., & Nguyen, C. (2017). Does urbanisation reduce rural poverty? Evidence from Vietnam. Economic Modelling, 60 (1), 253–270. Asogwa, F. O., Amuka, J. I., Igwe, A. A., & Nkalu, C. N. (2020). Dynamics of population, urbanization, and economic growth. Asongu, S. A., & Acha-Anyi, P. N. (2019). The murder epidemic: A global comparative study. International Criminal Justice Review, 29 (2), 105–120. Baltagi, B. H. (2008). Forecasting with panel data. Journal of Forecasting, 27 (2), 153–173. Baum-Snow, N., & Pavan, R. (2013). Inequality and city size. Review of Economics and Statistics, 95 (5), 1535–1548. Behrens, K., Duranton, G., & Robert-Nicoud, F. (2014). Productive cities: Sorting, selection, and aggregation. Journal of Political Economy, 122 (3), 507–553. Bloch, R., Monroy, J., Fox, S., & Ojo, A. (2015). Urbanisation and urban expansion in Nigeria (URN) Research Report. London: ICF International. Bloom, D. E., Canning, D., & Fink, G. (2008). Urbanisation and the wealth of nations. Science, 319 (5864), 772–775. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87 (1), 115–143. Bond, S. R. (2002). Dynamic panel data models: A guide to micro data methods and practice. Portuguese Economic Journal, 1 (2), 141–162. Bond, S., & Windmeijer, F. (2002). Finite sample inference for GMM estimators in linear panel data models. Cenmap Working Paper Series No. CWP04/02. Institute of Fiscal Studies, London. Brülhart, M., & Sbergami, F. (2009). Agglomeration and growth: Cross-country evidence. Journal of Urban Economics, 65 (1), 48–63. Castells-Quintana, D. (2017). Malthus living in a slum: Urban concentration, infrastructure, and economic growth. Journal of Urban Economics, 98 , 158–173. Castells-Quintana, D. (2018). Beyond Kuznets: Inequality and the size and distribution of cities. Journal of Regional Science, 58 (3), 564–580. Castells-Quintana, D., Ramos, R., & Royuela, V. (2015). Inequality in European regions: Recent trends and determinants. Review of Regional Research, 35 (2), 123–146. Chen, B., Liu, D., & Lu, M. (2017). City size, migration, and urban inequality in the People’s Republic of China (No. 723). ADBI Working Paper Series. Christiaensen, L., & Weerdt, J. D. (2017). Urbanisation, growth, and poverty reduction: The role of secondary towns. Final Report. International Growth Center. Clark, R. (2013). Convergence in national income distributions. Social Forces, 92 (2), 413–436. Cottineau, C., Finance, O., Hatna, E., Arcaute, E., & Batty, M. (2019). Defining urban clusters to detect agglomeration economies. Environment and Planning B: Urban Analytics and City Science . Dagum, C. (1997). Decomposition and interpretation of Gini and the generalized entropy inequality measures. Statistica, 57 (3), 295–308. Dang, V. A., Kim, M., & Shin, Y. (2015). Searching robust methods for dynamic panel data models in empirical corporate finance. Journal of Banking & Finance, 53 (1), 84–98. De Bruin, A., & Liu, N. (2020). The urbanization-household gender inequality nexus: Evidence from time allocation in China. China Economic Review, 60 (3), 101–131. Demont, M. (2013). Reversing urban bias in African rice markets: A review of 19 national strategies. Journal of Agricultural and Resource Economics, 38 (3), 433–453. Dorn, F., & Schinke, C. (2018). Top income shares in OECD countries: The role of government ideology and globalization. The World Economy, 41 (9), 2491–2527. Duranton, G. (2015). Growing through cities in developing countries. World Bank Research Observer, 30 (1), 39–73. Elhorst, J. P. (2014). Dynamic spatial panels: Models, methods, and inferences. In Spatial Econometrics (pp. 95–119). Springer, Berlin, Heidelberg. Fossaceca, A. (2019). Assessing the determinants of the human development index in oil-dependent nations. Undergraduate Economic Review, 16 (1), 1–14. Gini, C. (1909). Concentration and dependency ratios in the population. The American Journal of Sociology, 15 (2), 211–224. Guo, C., Buchmann, C. M., & Schwarz, N. (2019). Linking urban sprawl and income segregation: Findings from a stylized agent-based model. Environment and Planning B: Urban Analytics and City Science, 46 (3), 469–489. Ha, N. M., Le, N. D., & Trung-Kien, P. (2019). The impact of urbanization on income inequality: A study in Vietnam. Journal of Risk and Financial Management, 12 (3), 146–160. Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica: Journal of the Econometric Society, 50 (1), 1029–1054. Hardoon, D., Ayele, S., & Fuentes-Nieva, R. (2016). An economy for the 1%. Nairobi: Oxfam International. Harris, J. R., & Todaro, M. P. (1970). Migration, unemployment, and development: A two-sector analysis. The American Economic Review, 60 (1), 126–142. Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46 (6), 1251–1271. Heston, A., Summers, R., & Aten, B. (2012). Center for international comparisons of production, income, and prices at the University of Pennsylvania. Penn World Table Version, 7 (1). Hsiao, C., Pesaran, M. H., & Tahmiscioglu, A. K. (2002). Maximum likelihood estimation of fixed effects dynamic panel data models covering short periods. Journal of Econometrics, 109 (1), 107–150. Ike, G. N., Usman, O., & Sarkodie, S. A. (2020). Testing the role of oil production in the environmental Kuznets curve of oil-producing countries: New insights from the method of moments quantile regression. Science of the Total Environment, 711 (2), 135–208. Ikwuyatum, G. (2016). Migration and urbanization: Exploring the factors of the nexus in Nigeria. International Journal of Humanities and Social Science, 6 (8), 161–175. Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115 (1), 53–74. Jenkins, S. P. (2015). World income inequality databases: An assessment of WIID and SWIID. The Journal of Economic Inequality, 13 , 629–671. Kanbur, R., & Zhuang, J. (2013). Urbanization and inequality in Asia. Asian Development Review, 30 (1), 131–147. Khan, A. U., Saboor, A., Ali, I., Malik, W. S., & Mahmood, K. (2016). The urbanization of multidimensional poverty: Empirical evidence from Pakistan. Quality and Quantity, 50 (1), 439–469. Khan, H., Khan, I., & Binh, T. T. (2020). The heterogeneity of renewable energy consumption, carbon emission, and financial development globally: A panel quantile regression approach. Energy Reports, 6 (2), 859–867. Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99 (3), 483–499. Kuznets, S. (1955). Economic growth and income inequality. The American Economic Review, 45 (1), 1–28. Lee, C. (2005). Income inequality, democracy, and public sector size. American Sociological Review, 70 (1), 158–180. Lengyel, I., & Szakálné Kanó, I. (2012). Competitiveness of Hungarian urban micro-regions: Localization agglomeration economies and regional competitiveness function. Regional Statistics, 2 (1), 27–44. Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108 (1), 1–24. Lewis, W. A. (1954). Economic development with unlimited supplies of labor. The Manchester School, 22 (5), 139–191. Li, J., Cheong, T. S., Shen, J., & Fu, D. (2019). Urbanisation and rural-urban consumption disparity: Evidence from China. The Singapore Economic Review, 64 (4), 983–996. Li, Y., & Liu, X. (2018). How did urban polycentricity and dispersion affect economic productivity? A case study of 306 Chinese cities. Landscape and Urban Planning, 173 (2), 51–59. Li, Y., Chiu, Y., & Weng, W. (2020). The rise of the service sector and income inequality in China. China Economic Review, 59 (1), 101–132. Liu, Z., & Yang, Z. (2018). Urban agglomerations and income inequality: Evidence from Chinese cities. Urban Studies, 55 (10), 2069–2086. Liu, C., & Hsieh, E. (2020). Spatial dynamics of income inequality in the developing world: The case of Southeast Asia. Growth and Change, 51 (2), 616–640. López, R. E., & Rodríguez, F. A. (2018). Urbanisation and income inequality: Evidence from Latin American countries. Urban Studies, 55 (7), 1520–1538. Lutz, W., Sanderson, W., & Scherbov, S. (2008). The coming acceleration of global population ageing. Nature, 451 (7179), 716–719. Maket, I. J., Kanó, I. S., & Vas, Z. B. (2022). Reverse causal nexus between pro-poor policies and income inequality in Kenya. Regional Science Policy & Practice , https://doi.org/10.1111/rsp3.12540. Maket, I., Kanó, I. S., & Vas, Z. (2023). Estimations of the pooled dynamic panel data model with time-space dependence of selected Sub-Saharan African urban agglomerations, 2000-2020. Regional Statistics, 13 (4), 1–23. Moretti, E. (2012). The new geography of jobs. Houghton Mifflin Harcourt . Newman, P., & Kenworthy, J. R. (2015). The geography of sustainable development. Urban Studies, 52 (10), 1956–1976. North, D. C. (1990). Institutions, institutional change, and economic performance. Cambridge University Press. O’Donnell, O., Van Doorslaer, E., & Wagstaff, A. (2015). Analyzing health equity using household survey data. World Bank . Piketty, T. (2014). Capital in the twenty-first century. Harvard University Press . Ravallion, M., & Chen, S. (2007). China's (uneven) progress against poverty. Journal of Development Economics, 82 (1), 1–42. Sachs, J. D. (2005). The end of poverty: Economic possibilities for our time. Penguin Books . Sen, A. (1999). Development as freedom. Oxford University Press . Sheng, L., & Zeng, Q. (2021). A measure of the impact of urbanization on income inequality in China. The Journal of Chinese Economic and Business Studies, 19 (4), 381–399. Stern, D. I. (2017). The environmental Kuznets curve: A primer. Environmental Economics and Policy Studies, 19 (1), 51–61. Tiwari, A. K., & Sharma, S. (2020). Impact of urbanisation and industrialisation on income inequality in developing countries. Social Indicators Research, 153 (1), 1–20. Yousefi, V. (2019). Inequality and its impacts on economic growth in developing countries. Economic Analysis and Policy, 63 (1), 49–60. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5902961","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":411492403,"identity":"d46a06f7-31df-4243-a4ca-9960c24af7a4","order_by":0,"name":"Etienne Nzabirinda","email":"data:image/png;base64,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","orcid":"","institution":"University of Rwanda","correspondingAuthor":true,"prefix":"","firstName":"Etienne","middleName":"","lastName":"Nzabirinda","suffix":""}],"badges":[],"createdAt":"2025-01-25 17:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5902961/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5902961/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75739782,"identity":"26a613cc-7f23-4346-a6c0-8cdbebd19485","added_by":"auto","created_at":"2025-02-07 16:17:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21389,"visible":true,"origin":"","legend":"\u003cp\u003eGDP per capita in Sub Saharan Africa\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003eDenotes countries with an average GDP per capita growth rate of more than 1 percent over the period from 1996 to 2023.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5902961/v1/002338626029fd0fb01edda6.png"},{"id":75739784,"identity":"00eb60d0-e1ce-44e8-af6b-b89aec66c2ef","added_by":"auto","created_at":"2025-02-07 16:17:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":266641,"visible":true,"origin":"","legend":"\u003cp\u003eEmployment in agriculture vs. urban population, 1991–2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Ritchie and Roser (2018), CC BY.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5902961/v1/8bc22e579fc4b927a4544bc9.png"},{"id":75740416,"identity":"0b20801e-ceb0-4f12-9e31-e4ceed4b47c8","added_by":"auto","created_at":"2025-02-07 16:25:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31505,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive statistics of urbanization, income inequality, and climate factors contribute to high living costs in cities across Sub-Saharan Africa\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5902961/v1/8514ac5d00152d437b3a9792.png"},{"id":75739786,"identity":"ad2b8305-f0dd-4ee1-9edd-876cc992f8c5","added_by":"auto","created_at":"2025-02-07 16:17:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":198130,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the largest cities by population in sub-Saharan Africa\u003c/p\u003e\n\u003cp\u003eSource: UNECA (2017)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5902961/v1/07ba02503ea0f0a3b428b483.png"},{"id":77388424,"identity":"a2eceb33-e304-40c9-9ee8-090c2243bac0","added_by":"auto","created_at":"2025-02-28 06:02:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1660923,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5902961/v1/e99b942c-75ff-434d-ad74-cf91245cba13.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"High Living Costs, Urban Agglomeration, and Inequality: Lessons from the Spatial Geo-Economic Politics of Urban Out-Migration in Sub-Saharan African Cities.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the last two decades, sub-Saharan Africa has seen a significant increase in its urban population, from 133\u0026nbsp;million in 1990 to over 360\u0026nbsp;million in 2022, making it one of the fastest urbanizing regions globally (United Nations, 2022). This demographic shift presents new economic opportunities and urban development prospects, but also brings about complex challenges, particularly for low and middle-income households. A major issue in cities is the high cost of living, which widens the gap between the wealthy and the poor, leading many to seek more affordable living arrangements outside of urban areas.\u003c/p\u003e \u003cp\u003eThis results in a shortage of housing and essential services, driving up property prices and rent. This exacerbates the housing crisis, forcing many to live in informal settlements. According to UN-Habitat (2020), over 58% of the urban population in sub-Saharan Africa resides in slums with inadequate infrastructure and living conditions. Cities like Lagos and Kinshasa struggle to provide affordable housing for their growing populations, leading to a reliance on informal housing (Cira, Kamunyori, \u0026amp; Babbar, 2016).\u003c/p\u003e \u003cp\u003eApart from housing, urban households in sub-Saharan Africa face higher costs for basic necessities like food and utilities compared to rural households. Studies show that urban households pay 20 to 30% more for essential goods due to inefficient logistics and underdeveloped infrastructure, impacting food security and financial stability (Lall, Henderson, \u0026amp; Venables, 2021; Frayne, Crush, \u0026amp; McLachlan, 2014).\u003c/p\u003e \u003cp\u003eUrban agglomeration, the concentration of population and economic activities in specific urban centers, exacerbates inequalities. Wealthier households have better access to services like healthcare, education, and transportation, while poorer communities lack resources. Foreign investments and economic growth often favor affluent areas, widening socio-economic disparities (Satterthwaite \u0026amp; Tacoli, 2020).\u003c/p\u003e \u003cp\u003eAs a result, many low-income households move to peri-urban and rural areas to afford a decent living. However, these areas also face challenges like inadequate infrastructure and limited access to services, straining the local economy and accommodating the influx of migrants (Potts, 2020; Fox, 2021).\u003c/p\u003e \u003cp\u003eThis paper aims to examine the factors driving urban out-migration in sub-Saharan Africa, including high living costs, urban agglomeration, and inequality. It explores how these factors interact within a broader geo-economic and political context, and suggests governance reforms and investments in human capital as potential solutions to address these challenges. The paper is organized as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the literature review, Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the methodology, Section \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e4\u003c/span\u003e sets out the analysis and results, and Section \u003cspan refid=\"Sec24\" class=\"InternalRef\"\u003e5\u003c/span\u003e concludes with policy recommendations.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe following literature review synthesizes key research and theoretical frameworks relating to urbanization, high living costs, and economic disparities in Sub-Saharan Africa, situating the dynamics that drive urban out-migration and urban agglomeration in the region.\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 High Living Costs in SSA Cities\u003c/h2\u003e\n \u003cp\u003eThe cost of living in most urban centers within SSA has increased, especially in major cities like Lagos, Nairobi, and Kinshasa. This increase is mainly attributed to housing shortages, inflation, and the concentration of wealth and economic opportunities in urban areas (UN-Habitat, 2020). Increases in housing costs are among the leading drivers of high living costs. For instance, house prices have increased up to 80% in just five years in some West African cities such as Abidjan and Douala, exacerbating the affordability crisis (UN-Habitat, 2023). According to the World Bank (2022), more than 60% of urban residents in SSA live in informal settlements with inadequate access to basic services and infrastructure. The informality of these settlements often leads to high density, poor sanitation, and low-quality housing, increasing economic vulnerability for low-income urban populations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Urbanization and Economic Growth in Sub-Saharan Africa\u003c/h2\u003e\n \u003cp\u003eRapid urbanization has characterized Sub-Saharan Africa, where a considerable population has migrated into urban centers in pursuit of better opportunities. By 2020, the urban population had reached 43%, and projections are that by 2050, this proportion will exceed 60% according to the United Nations high variant projection series, 2022. This is basically due to increased migration from rural areas for better job prospects, education, and healthcare. However, while urbanization in SSA is among the fastest-growing in the world (World Bank, 2022), it has also caused a growing gap between the rich and the poor. Economies have benefited unevenly from urbanization, and though the population may be growing in these cities, many residents face growing inequalities in incomes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Economic Disparities and Migration Patterns\u003c/h2\u003e\n \u003cp\u003eThis would be intrinsically related to rising economic disparities between urban and rural regions in SSA, while there is better remuneration and opportunities for jobs in urban centers, yet the cost of housing, transportation and basic services remain prohibitively high. For example, cities like Nairobi have a per capita GDP of approximately USD 2,400 and house costs alone take up as high as 70% of household incomes for many residents (World Bank, 2022). The rural areas are underdeveloped economically but have comparative advantages in terms of lower living costs, adding to their appeal among people unable to afford city lifestyles. This growing economic disparity often drives people back to rural areas or smaller, more affordable cities. According to UN-Habitat (2023), over 30% of the urban population in cities such as Mombasa and Kigali has migrated to smaller urban areas because larger cities have become unaffordable. This phenomenon, known as \u0026ldquo;urban displacement,\u0026rdquo; is becoming more pronounced in cities experiencing rapid population growth and high levels of economic inequality (Tacoli, 2017).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Urban Agglomeration and Informal Economies\u003c/h2\u003e\n \u003cp\u003eUrban agglomeration in SSA cities is characterized by the growth of large urban areas with high population densities, often without adequate planning or infrastructure.\u003c/p\u003e\n \u003cp\u003eAs cities expand, informal economies have become a defining feature of urban life. According to the International Labor Organization, 2021, it is estimated that more than 60% of the workforce in SSA cities are found in the informal sector, mostly in low-paid jobs that make them very susceptible to high living costs. This is exacerbated by the mushrooming of informal settlements in cities like Nairobi and Lagos, where basic services and infrastructure are lacking. Informal economies thrive in urban agglomerations where low-income migrants often settle on the periphery of the city to avoid high costs in city centers. However, these peripheries remain economically peripheral, exacerbating economic inequalities (Satterthwaite, 2016).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.5 Agglomeration Economics\u003c/h2\u003e\n \u003cp\u003eAgglomeration economics are the economic efficiencies that result from the concentration of economic activities within cities. These advantages are due to the underlying two fundamentals: division of labor and economies of scale.\u003c/p\u003e\n \u003cp\u003eDivision of labor enables productivity increase due to specialization which in turn provides growth. The cities have the added advantages of this phenomenon of specialization in firms, industries, and functions that give rise to local advantages and further economic development (Duranton \u0026amp; Puga, 2004). External economies of scale, or agglomeration economies, further facilitate the process by reducing transaction costs such as transport and communication costs, while promoting knowledge spillovers in the process (Glaeser et al., 1992). These agglomeration economies are usually categorized into:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1. Localization Economies\u003c/strong\u003e: Firms within the same industry benefit from being close to one another whereby labor, suppliers, and customers can easily share.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2. Urbanization Economies\u003c/strong\u003e: Firms from different industries benefit from proximity to a variety of economic activities since the latter may enhance productivity and cause innovation.\u003c/p\u003e\n \u003cp\u003eIn the urban context, these economies make it possible to ensure better firms-labor matching, the spreading of the same infrastructures, and learning and innovation processes on the part of cities. Added to this, these can well be further causes determining the dynamism of local economies and their phases of specialization of services (Krugman, 1991; Marshall, 1890).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.6 Evidence from Sub-Saharan Africa\u003c/h2\u003e\n \u003cp\u003eAlthough very few studies focus on agglomeration economics in the SSA region, studies that compare patterns of urbanization across regions suggest that in SSA this process is not always accompanied by economic outcomes similar to those in other developing regions. While urbanization is associated with economic growth and poverty reduction in other developing regions such as Latin America and Asia, that is not quite the case for SSA (Gollin et al., 2013; World Bank, 2015).\u003c/p\u003e\n \u003cp\u003eStudies by Behrman et al. (2007) and the World Bank (2016) found that the levels of urbanization in SSA might not necessarily return the expected dividends with regard to reduced poverty levels and higher economic growth. This anomaly underscores the difficulty of realizing economic returns on urbanization in SSA, and perpetuates inequalities at the core of migration imperatives.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e2.7 Sub-Saharan Africa Context\u003c/h2\u003e\n \u003cp\u003eSub-Saharan Africa continues to be one of the fastest urbanizing parts of the world, with millions moving from rural areas into the urban centers in search of economic opportunities. However, high living costs in urban areas often push migrants into informal settlements where social networks and community support can help mitigate the challenges of relocation. Such is the argument by authors like Tacoli (2009) and Selod \u0026amp; Shilpi (2021).\u003c/p\u003e\n \u003cp\u003eThese informal settlements exacerbate inequalities, as they are often located in areas with insufficient infrastructure and lack access to essential services. This further increases the socio-economic divide between urban residents due to the failure of urban centers to provide reasonable housing and basic services, hence fueling migration pressures. People migrate circularly - that is, moving back and forth between rural and urban areas - searching for an equilibrium between higher living costs in urban areas with the relative affordability of rural areas (Serdeczny et al., 2017). Researchers such as Mueller et al. (2020) and Fields (1982) support that policies to be taken to reduce economic disparities, housing policy, and access to basic services will help alleviate pressures on out-migration in SSA.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Source of data\u003c/h2\u003e\n \u003cp\u003eData for this study are extracted and combined from several databases, namely: The SWIID provides the Gini index to assess the level of income inequality across Sub-Saharan Africa. World Bank data on urbanization, industrialization, and GDP helps analyze economic growth and urban expansion. The UNDP offers urbanization rates, highlighting the rapid growth of cities in the region. Governance metrics provided by World Governance Indicators, crucial to the understanding of how effective policies affect living conditions. Finally, it uses the World Penn Tables Human Capital Index for education and labor market participation-both basic causes of migration and inequality.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariable description, measurements, and data sources\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeasure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData Source\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome Inequality (-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGini index (lagged by 1 period)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSWIID\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban Share\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban share (%) of population (total urban population\u0026thinsp;\u0026divide;\u0026thinsp;total country\u0026rsquo;s population)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban Share.Sq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSquare of the urban share (%) of population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban Share\u0026thinsp;\u0026gt;\u0026thinsp;1M\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShare (%) of urban population in agglomerations of more than 1\u0026nbsp;million people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrbanization Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRatio of urban population to rural population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank/UNDP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrbanization Rate.Sq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSquare of the urbanization rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank/UNDP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP per Capita Growth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP Per Capita Growth (annual % change)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP per Capita Growth.Sq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSquare of the GDP per Capita Growth (annual % change)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndustrialization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShare (%) of population employed in urban industries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovernance Policy Preference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernance Effectiveness Index (proxy measure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWGI/World Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHuman Capital Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Capital Index per person (based on years of schooling and returns to education)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Penn Tables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Author (2024)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAccording to sources of tables,1 data for this study was gathered from sources of international reliability. Gini index related to income inequality has been sourced from SWIID while urbanization, GDP Growth, Industrialization, and employment in urban industries\u0026apos; information is sourced out from World Bank, UNDP provides data related to Urbanization while Governance Effectiveness Index from WGI (World Governance Indicators) has been availed. Finally, human capital from educational attainment is captured by World Penn Tables. These data ensure consistency and credibility for analysis of economic factors determining urban out-migration in Sub-Saharan Africa.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Study setting\u003c/h2\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 GDP per capita and urban agglomeration in Sub-Saharan African Cities.\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the very low GDP per capita, averaging at \u003cspan\u003e$\u003c/span\u003e1,600 in Sub-Saharan Africa in 2022, contributes to rising income inequalities, especially in urban areas. Even with low incomes, rapid growth has marked cities like Lagos and Nairobi, with housing costs having increased by 50\u0026ndash;80% in the past ten years. In Nairobi, for instance, despite the GDP per capita being \u003cspan\u003e$\u003c/span\u003e2,400, about 60% of the population lives in informal settlements that are characterized by inadequate infrastructure. High living costs and low incomes make urban life unaffordable for many, who then move to city fringes or rural areas where living is cheap but economic opportunities are limited. Although cities like Lagos have higher GDPs, the growth is concentrated in certain sectors, leaving the majority of the population behind and contributing to continued rural population growth, although at a slower rate of 1.2% annually.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2: Employment in agriculture vs. urban population, 1991\u0026ndash;2024.\u003c/h2\u003e\n \u003cp\u003eFrom Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, between 1991 and 2024, Sub-Saharan Africa experienced a shift from agriculture to urbanization. Agricultural employment decreased from 65\u0026ndash;45%, while urban populations grew from 25\u0026ndash;30% to over 50%. This migration to urban areas has created economic opportunities but also exacerbated inequality, as many people work in informal sectors. The decline in agricultural jobs raises concerns about food security, as urban areas become more reliant on food imports, while rural regions face labor shortages. This trend is evident in cities such as Kinshasa (DRC), Lagos (Nigeria), Johannesburg (South Africa), Nairobi (Kenya), Luanda (Angola), Dar es Salaam (Tanzania), Accra (Ghana), Abidjan (Ivory Coast), Douala (Cameroon), and Kampala (Uganda)\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.3 Descriptive statistics and stylized facts\u003c/h2\u003e\n \u003cp\u003eBased on Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, in Sub-Saharan African cities, high living costs are influenced by a range of factors including urbanization, income inequality, industrialization, and environmental conditions. For instance, Abidjan and Accra, with high urbanization rates of 61.17% and 56.58% respectively, are experiencing increased living costs due to overcrowding, limited affordable housing, and strained infrastructure. Apart from these challenges, they also have to address significant income disparities: Abidjan with a Gini coefficient of 40.60 and Accra with a Gini coefficient of 42.10, characterized by high wealth disproportions that continue to push poor people to slums, increasing vulnerability to price increases. Like Abuja with 56.25% and Lagos with 60.34% also face high living costs due to fast-growing urbanization and higher income disparities, especially Lagos with a Gini coefficient of 50.31. As these cities expand, economic opportunities become limited, leading to overcrowding and price inflation in everyday goods. In contrast, Durban and Johannesburg in South Africa are largely industrialized, with Johannesburg having a higher industrial share of 23.44%. The industrial absorption may have partly eased the pressure on living costs by creating job opportunities for residents. However, income inequality, with a Gini coefficient of 0.63 for Johannesburg, still causes disparities in wealth distribution and fuels migration into less urbanized areas with lower costs.\u003c/p\u003e\n \u003cp\u003eLuanda, with a high urbanization rate of 50.77% and a Gini coefficient of 1.00, experiences very high living costs despite relatively modest economic growth of a 1.02% GDP per capita increase. Industrialization in Luanda and cities like Lusaka, which has an industrialization rate of 11.99%, tends to raise living costs due to environmental consequences such as high carbon emissions, especially in Luanda, which emits 28,330 kt of CO2, further escalating living expenses. These high costs push many residents to seek affordable living conditions in rural or peri-urban areas, resulting in urban out-migration.\u003c/p\u003e\n \u003cp\u003eOn the other hand, Mogadishu, Kampala, and Dodoma have relatively better climatic conditions that lower the cost of living, reflected in relatively lower food prices and increased food security due to improved agricultural productivity. However, they have relatively weaker agglomeration with urbanization rates of 39.35% and 42.87% compared to Kampala and Dodoma, reducing the pressure associated with living costs compared to cities like Kinshasa (79.11%) and Dar es Salaam (74.78%), which show high overcrowding.\u003c/p\u003e\n \u003cp\u003eNairobi, with a high level of industrialization and an urbanization rate of 66.11%, faces high demand for services and infrastructure that raise the cost of living, despite a diversified economy. Its growth rate, driven by industrialization, has not yet managed to alleviate inequality in the pressure it exerts on housing and basic services, forcing residents to move to rural areas in search of affordable living.\u003c/p\u003e\n \u003cp\u003eCities like Windhoek, Pretoria, and Cape Town, with higher levels of industrialization and relatively stable economic growth, offset some of the pressures on the cost of living. Even in those cities, however, youth unemployment acts as a major driver for migration. For example, Pretoria and Johannesburg have very high rates of youth unemployment among the growing population, forcing them to seek better opportunities elsewhere.\u003c/p\u003e\n \u003cp\u003eApart from Lusaka and Harare, where a fair proportion of their populations live in slums or informal settings, driving up living costs due to inadequate infrastructure and unequal access to services, Juba and Maputo report higher costs driven by rapid urbanization without suitable infrastructure or resources available to serve the expanded population, resulting in accentuated inequity.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.4 Study of geographic location of largest agglomeration city in sub-Saharan Africa\u003c/h2\u003e\n \u003cp\u003eFrom Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, cities such as Kinshasa, Lagos, Johannesburg, Nairobi, Luanda, Dar es Salaam, Accra, Abidjan, Douala, and Kampala illustrate the complex challenges of rapid urbanization in Sub-Saharan Africa. As these cities grow, living costs rise sharply due to increased demand for housing, goods, and services. For instance, in Lagos and Kinshasa, housing prices are soaring, forcing many to live in informal settlements with limited infrastructure. Urban agglomeration creates economic opportunities but also strains resources, with cities like Nairobi and Accra becoming hubs of commerce while grappling with overcrowded streets and underdeveloped public services. Inequality remains a key issue, with wealth concentrated in specific areas while large portions of the population work in informal sectors with low wages and poor conditions, as seen in cities like Abidjan and Douala.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Econometric Model\u003c/h2\u003e\n \u003cp\u003eA dynamic econometric model estimated using GMM will be employed to analyze high living costs, urban agglomeration, inequality, and urban out-migration in Sub-Saharan African cities. Following the stylized UE model of Bertinelli and Black (2004), we take a step further with an empirically testable prediction: the quality of urban infrastructure significantly influences economic performance by enhancing the benefits of urban agglomeration. Equations\u0026nbsp;(1) are rewritten as follows:\u003c/p\u003e\n \u003cp\u003e\u0026Delta;Y(e,t)=\u0026alpha;\u0026thinsp;+\u0026thinsp;\u0026beta;log(Y(e,0))+\u0026gamma;1HHI(e,0)+\u0026gamma;2UI(e,0)+\u0026sum;n\u0026thinsp;=\u0026thinsp;1N\u0026delta;nX(e,t)n+ϵ(e,t) (1)\u003c/p\u003e\n \u003cp\u003ewhere UI(e,0) represents the urban-specific quality of urban infrastructure. Regression analysis across nations, based on Eq.\u0026nbsp;(1), is conducted using pooled OLS. The main focus is on the panel dynamic model regression estimated using system GMM suggested by Arellano and Bover (1995). Eq.\u0026nbsp;(2) is rewritten in dynamic panel format:\u003c/p\u003e\n \u003cp\u003e\u0026Delta;Y(e,t) = \u0026alpha;\u0026thinsp;+\u0026thinsp;\u0026beta;log(Y(e,t-1)) + \u0026gamma;1HHI(e,t-1) + \u0026gamma;2UI(e,t-1) + \u0026rho;\u0026sum;n\u0026thinsp;=\u0026thinsp;1N\u0026delta;nX(e,t)n\u0026thinsp;+\u0026thinsp;\u0026micro;e\u0026thinsp;+\u0026thinsp;\u0026tau;t + ϵ(e,t) ( 2)\u003c/p\u003e\n \u003cp\u003eHere, t denotes the five-year interval period, and \u0026micro;e, \u0026tau;t, and ϵ(e,t) are error terms. Eq.\u0026nbsp;(3) is estimated in a simple AR(1) specification that includes the interaction term of urban agglomeration and urban infrastructure:\u003c/p\u003e\n \u003cp\u003eY(e,t) = \u0026alpha;\u0026thinsp;+\u0026thinsp;\u0026beta;log(Y(e,t-1)) + \u0026gamma;1HHI(e,t-1) + \u0026gamma;2UI(e,t-1) + \u0026gamma;3(HHI(e,t-1)*UI(e,t-1)) +\u0026rho;\u0026sum;n\u0026thinsp;=\u0026thinsp;1N\u0026delta;nX(e,t)n\u0026thinsp;+\u0026thinsp;\u0026micro;e\u0026thinsp;+\u0026thinsp;\u0026tau;t + ϵ(e,t) (3 )\u003c/p\u003e\n \u003cp\u003eThe interaction of urban agglomeration and urban infrastructure is presented by multiplying the two proxies, i.e., HHI(e,t-1)*UI(e,t-1). A country-specific effect denoted by \u0026tau;t captures time-invariant determinants of GDP per capita, which may or may not be correlated with urban agglomeration, motivating the panel regression estimation. To identify the drivers of urban out-migration in Sub-Saharan Africa by analyzing how high living costs, urban agglomeration, and income inequality influence internal migration. The Dynamic Panel Data Model will be applied to describe how changes in these factors affect migration across cities in Sub-Saharan Africa over time. The econometric model used for estimation is specified as follows:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMigration it\u003c/strong\u003e\u0026thinsp;=\u0026thinsp;\u0026alpha;\u0026thinsp;+\u0026thinsp;\u0026beta;1Income inequalityit-1\u0026thinsp;+\u0026thinsp;\u0026beta;2Urbanization Rate it\u0026thinsp;+\u0026thinsp;\u0026beta;3Night-Time Lights it\u0026thinsp;+\u0026thinsp;\u0026beta;4GDP per capita it\u0026thinsp;+\u0026thinsp;\u0026beta;5Slum%it\u0026thinsp;+\u0026thinsp;\u0026beta;6Youth Unemployment it\u0026thinsp;+\u0026thinsp;\u0026beta;7Food insecurity it\u0026thinsp;+\u0026thinsp;\u0026beta;8Agglomeration it\u0026thinsp;+\u0026thinsp;\u0026beta;9Health expenditure it + ϵit (4)\u003c/p\u003e\n \u003cp\u003eurban migration in Sub-Saharan Africa is largely driven by a combination of high living costs, urban agglomeration, and economic inequality. As cities grow, rising costs and overcrowding push many people, particularly from poorer backgrounds, to migrate to less populated areas or rural regions. Income inequality within cities further exacerbates these migration patterns, with poorer individuals often unable to afford basic services or housing in urban centers. Youth unemployment and food insecurity also play significant roles in driving people to seek better opportunities elsewhere. In some cases, economic growth in cities may create new opportunities but also increase living costs, making migration a complex balancing act between seeking jobs and dealing with urban pressures.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Statistics of data\u003c/h2\u003e \u003cp\u003eThis section presents descriptive statistics and stylized facts based on an analysis of urban growth, inequality, and migration trends in Sub-Saharan Africa. The data highlights the factors influencing whether high living costs and urban agglomeration push individuals to migrate out of cities. By examining these variables, the analysis identifies the patterns that suggest a connection between rising urban living costs, the concentration of economic activities, and the increasing disparities within urban populations, which may compel people to leave for more affordable and less congested areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eshows urban, economic, social, and environmental trends in Sub-Saharan cities.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Net migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Income inequality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban rate (% of total population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Urban rate (past five years) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Night-time lights\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog GDP per capita (log)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog GNI (annual growth, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Employment in industry (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Slums (% of urban population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Agglomeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Employment in agriculture (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Health expenditure (% of GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Mobile cellular subscriptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.6770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Current education expenditure (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Agricultural land (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Forest area (% of land area)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Youth unemployment (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Food insecurity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides descriptive statistics of Sub-Saharan African cities, offering critical insights into the nature of urban out-migration. This phenomenon is characterized by high living costs, agglomeration, and inequality. The net migration rate averages 1.092, indicating a significant inflow into urban centers, particularly in cities like Lagos, Kinshasa, and Johannesburg, which serve as economic hubs despite facing challenges such as housing shortages. In contrast, migration rates are low for cities like Mogadishu and Juba due to political turmoil.\u003c/p\u003e \u003cp\u003eIncome inequality is high, with a Gini coefficient of 0.81, leading to a concentration of wealth in cities such as Johannesburg and Lagos. A large proportion of residents in these cities experience poverty and lack access to services, motivating migration towards cities with more economic opportunities. The average urbanization rate is 4.403, with high growth rates in cities like Abuja, Accra, and Kampala putting pressure on housing and infrastructure. In contrast, cities like Windhoek and Dodoma grow at a slower rate, allowing for more manageable expansion.\u003c/p\u003e \u003cp\u003eOver the last five years, the average urban growth rate has been 5.287, resulting in intense sprawl in cities like Lagos, Nairobi, and Johannesburg, straining resources and services. Nighttime light intensity averages 6.40, indicating high demand and economic activity in cities like Lagos and Kinshasa, leading to increased living costs. Cities such as Abuja, Johannesburg, and Cape Town have a higher GDP per capita at 7.45, though this wealth is still unequally distributed.\u003c/p\u003e \u003cp\u003eGNI growth is at 1.25, showing steady economic expansion in cities like Accra, with slower growth in Kinshasa and Mogadishu limiting job creation and prompting migration. Industrial employment remains low, with more opportunities in Cape Town and Mogadishu needing to be created. The average slum population is 0.310, highlighting urban housing crises that intensify migration due to inadequate living standards.\u003c/p\u003e \u003cp\u003eAgglomeration, averaging 4.650, reflects economic processes that ensure growth while increasing living cost inequality. Youth unemployment averages 1.690, posing a significant challenge in cities like Kinshasa, Lagos, and Mogadishu, leading to increased migration in search of better job prospects. Food insecurity, averaging 4.990, also contributes to migration, with cities like Kinshasa and Lagos struggling to provide affordable and nutritious food to their populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Linear GMM Estimation results\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear GMM Estimation results highlight the interplay between urban dynamics, economic geography, and inequality, specifically focusing on the drivers of urban out-migration in Sub-Saharan African cities.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u0026thinsp;\u0026gt;\u0026thinsp;Z\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1823*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Income Inequality (Lag)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9784***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Urban Rate (% of Total Pop)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.5023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Urban Rate (Past 5 Years, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Night-Time Lights (Log)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7356*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog GDP per Capita (Log)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.0163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog GNI (Annual Growth, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2418***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Employment in Industry (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Slums (% of Urban Population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Agglomeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.2041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Employment in Agriculture (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1124***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Health Expenditure (% of GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.0754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Mobile Cellular Subscriptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Agricultural Land (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.0342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Forest Area (% of Land Area)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Youth Unemployment (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1854**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Food Insecurity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.0131**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;2.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNotes: Significance Levels: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e, \u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/b\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResults from Linear GMM Estimation in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e imply that the high cost of living, agglomeration, and inequality are key drivers of out-migration in urban dynamics for cities like Abidjan, Lagos, Nairobi, and Johannesburg. In Lagos, rapid urbanization results in informal settlements, housing shortages, and high rents, leading low-income residents to move to peri-urban areas or secondary cities like Ibadan. Similarly, in Nairobi, slum conditions and high food prices drive many inhabitants to migrate, as indicated by the coefficient for Log Slums of 0.0029.\u003c/p\u003e \u003cp\u003eUrban agglomeration presents challenges in cities like Johannesburg and Dar es Salaam. While Johannesburg's economic opportunities attract migrants, congestion, housing shortages, and inequality push others to nearby areas like Pretoria. Dar es Salaam faces similar issues with poor urban planning, leading to outward migration to regions like Dodoma. The negative coefficient for agglomeration (Log Agglomeration, coefficient: -0.2041) reflects this trend. The positive relationship of night-time lights (Log Night-Time Lights, coefficient: 0.7356) in cities like Nairobi and Cape Town shows how developed urban areas attract migration and contribute to population growth.\u003c/p\u003e \u003cp\u003eInequality is a significant issue in cities like Kinshasa and Luanda, where income disparities force marginalized groups to migrate. The positive and significant coefficient for income inequality (Log Income Inequality: 0.9784) highlights wealth concentration in urban centers. In cities like Harare and Mogadishu, high youth unemployment (Log Youth Unemployment: 0.1854) further drives migration trends.\u003c/p\u003e \u003cp\u003eTo address out-migration, targeted urban policies tailored to each city's challenges are necessary. A\u003c/p\u003e \u003cp\u003eaffordable housing programs and improved infrastructure can alleviate living costs in cities like Addis Ababa and Kampala. Cities like Lusaka, Juba, and Nairobi could benefit from policies promoting equitable economic growth, education, and youth job creation. Addressing these challenges will make Sub-Saharan African cities more inclusive and sustainable.\u003c/p\u003e \u003cp\u003eThe estimates from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e have important implications for Nairobi, where high living costs, agglomeration, and inequality drive out-migration. As the capital of Rwanda, Nairobi experiences rapid urbanization and high housing demand. High living costs push low-income residents to the outskirts, reflecting the impact of slum populations and housing affordability (Log Slums, coefficient: 0.0029). Despite infrastructure improvements, congestion and unequal resource distribution contribute to migration patterns (Log Agglomeration, coefficient: -0.2041).\u003c/p\u003e \u003cp\u003eIncome inequality is a major driver of urban migration in Nairobi, with wealth concentration limiting opportunities for low-income groups. The high coefficient for income inequality (Log Income Inequality: 0.9784) forces migration in search of better living standards. High youth unemployment (Log Youth Unemployment: 0.1854) further exacerbates migration trends.\u003c/p\u003e \u003cp\u003ePolicies in Nairobi should focus on reducing income inequality, providing affordable housing, and creating job opportunities for youth. Rural-urban integration, improved public services, and sustainable urban growth strategies are essential to address these challenges.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Non-linear estimates GMM Estimations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNon-linear estimates GMM Estimations results highlight the interplay between urban dynamics, economic geography, and inequality, specifically focusing on the drivers of urban out-migration in Sub-Saharan African cities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u0026thinsp;\u0026gt;\u0026thinsp;Z\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.231***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Income Inequality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.432**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Urban Rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.231**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;2.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Urban Rate (%)\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.032*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog GDP per Capita (Log)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;3.214***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;3.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog GDP per Capita (Log)\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.543**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Employment in Agriculture (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.763***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Slums (% of Urban Population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.419**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Employment in Agriculture \u0026times; Slums\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.032*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen\u0026rsquo;s J-statistic (Over-ID Test)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;2.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: Significance Levels: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/em\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: Non-linear GMM estimates provide significant insights into the spatial geo-economic dynamics driving urban out-migration in Sub-Saharan African cities such as Abidjan, Lagos, Nairobi, Johannesburg, and Kigali. High living costs, urban agglomeration, and inequality are all intertwined within the rapid urbanization-economic inequality matrix, influencing migration patterns.\u003c/p\u003e \u003cp\u003eIn cities like Lagos, Kinshasa, and Abidjan, slum populations (Log Slums, coefficient: 0.419) are among the most significant drivers of migration. Slums often have poor housing conditions, high living costs, and a lack of basic services that push residents to seek better opportunities outside metropolitan areas. For example, large informal settlements and high cost of living are major drivers of migration from Kinshasa. This interaction implies that Log Employment in Agriculture and Slums is less effective in absorbing migrants when urban poverty or slum conditions complicate rural-urban migration flows in cities like Dar es Salaam or Lusaka (coefficient: -0.032).\u003c/p\u003e \u003cp\u003eThe findings also elucidate the complex dynamics within the relationship between urbanization and migration. In cities like Nairobi and Johannesburg, where urbanization rates are high, initial urban growth tends to be associated with better economic opportunities that decrease the likelihood of migration (Log Urban Rate, coefficient: -1.231). In cities like Cape Town and Kigali, the positive non-linear effect of urbanization (Log Urban Rate (%), coefficient: 0.032) indicates that increased population density and resource strain may eventually increase migration pressures in those cities. For instance, Cape Town is experiencing rapid growth, particularly in informal settlements, and congestion in certain areas may drive residents to move to less congested areas.\u003c/p\u003e \u003cp\u003eAnother underlying cause of migration patterns is economic factors, especially GDP per capita. In cities like Pretoria and Durban, high GDP per capita (Log GDP per Capita, coefficient: -3.214) is typically associated with higher economic opportunities and lower out-migration. Conversely, in cities like Luanda and Abuja, rapid economic growth may lead to increasing inequalities; the Log GDP per Capita\u0026sup2; term (0.543) suggests that above a certain threshold of economic growth, migration pressures may rise due to increasing inequalities and overcrowding.\u003c/p\u003e \u003cp\u003eIncome inequality is also a significant push factor for migration across these cities. The coefficient for Log Income Inequality (0.432) indicates that urban centers with high inequality between the rich and the poor, such as Harare and Maputo, are prompting low-income earners to migrate in search of a more favorable living and economic environment. Cities like Addis Ababa and Mogadishu, despite being economic hubs, experience significant out-migration due to inequalities and lack of access to basic facilities.\u003c/p\u003e \u003cp\u003eRural employment also plays a considerable role. In cities like Juba, Dodoma, and Lusaka, rural employment opportunities, particularly in agriculture, serve as an attractive option for migrants from urban centers. The positive coefficient for Log Employment in Agriculture (0.763) demonstrates that agriculture remains a significant source of livelihood for many, especially in countries where the rural-urban divide is prominent. However, the interaction between agricultural employment and slum conditions suggests that slum-dwelling populations in cities like Kampala and Johannesburg may struggle to transition into agricultural work due to lack of resources or support for such a shift.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Robustness Check\u003c/h2\u003e \u003cp\u003eThe robustness of the results was assessed through Hansen's J-statistic test. First, alternative model specifications, including different lag structures and regional controls, confirmed that the key relationships between high living costs, urban agglomeration, and inequality remained consistent. Expanding the sample to include more cities like Kinshasa and Luanda did not alter the findings, indicating that the results are generalizable across different urban areas in SSA.\u003c/p\u003e \u003cp\u003eInstrument validity was confirmed through Hansen\u0026rsquo;s J-statistic and serial correlation tests, ensuring no endogeneity issues. Placebo tests also showed no significant effects, further validating the results. Non-linear relationships, particularly for GDP per capita and urbanization, were stable across different specifications. Sensitivity analyses with various data sources and time periods confirmed the robustness of the findings, supporting the conclusion that high living costs, inequality, and urbanization are key drivers of migration in Sub-Saharan African cities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Hansen\u0026rsquo;s J-statistic\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen's J-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe p-value of 0.183 follows Hansen's J-statistic for the Two-Stage Least Squares estimation model presented in Table\u0026nbsp;5. This reveals the validity of the instruments taken up by the model-that is, the consumption-investment instrumental variable does not necessarily correlate with the error term, meaning there isn't over-identification in this model. This means that the model can actually analyze the drivers of urban out-migration in some cities across Sub-Saharan Africa, including Abidjan, Abuja, Accra, Addis Ababa, Cape Town, and many others.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Concluding remarks","content":"\u003cp\u003eThis paper examines the high living costs, urban agglomeration, and inequality in cities of Sub-Saharan Africa using the spatial geo-economic politics of urban out-migration. Using the GMM technique with data from the World Bank, this study identified key drivers of migration and pointed to linkages between income inequality, rates of urbanization, and the cost of living. A very significant movement across the studied cities is marked by an average net migration rate of 1.092, whereas the presence of mega economic hubs like Lagos, Kinshasa, and Johannesburg on one side promotes immigration. However, simultaneously cities are afflicted by problems like a high cost of living and inadequate housing that make people move to cheaper and less congested areas, thus facilitating urban out-migration.\u003c/p\u003e \u003cp\u003eThe main determinants of migration are income inequality, with a Gini coefficient of 0.81. Cities like Johannesburg and Lagos demonstrate high concentrations of wealth in few areas and leave the rest of the population to reside in areas mostly without access to key services. This situation fuels migration as lower-income residents move to areas with more opportunity. Other cities like Abuja, Accra, and Kampala are growing at an average growth rate of 4.403% per year, thus contributing to the sprawl condition of these cities. For example, cities like Windhoek and Dodoma are growing at a relatively slower pace, which could ease pressure on infrastructure. The rapid growth of cities has been attributed to migration into the peri-urban or secondary cities in search of housing affordability and less congestion.\u003c/p\u003e \u003cp\u003eThis is further reflected in the GDP per capita, averaging 7.45, showing that there is growing wealth hand in hand with considerable poverty. A GNI growth rate of 1.25% indicates steady expansion in cities like Accra, though others, like Kinshasa and Mogadishu, are slow and cannot create many jobs, thus contributing to migration. The employment rate in industries is low at an average of 2.595%, while economic mobility within the cities remains restricted, especially in Cape Town, where higher economic output has been recorded. Slums themselves take a share of 0.310% of the urban population and are normally associated with housing crises, which in turn spur migration to other less costly places to live in, such as Lagos and Nairobi.\u003c/p\u003e \u003cp\u003eAgglomeration, with a mean score of 4.650, is a symptom of the concentration of economic activities in large cities, exacerbating living costs and social inequality. In cities like Johannesburg, the pull of opportunities attracts migrants, while housing shortages and congestion push others to seek less crowded areas. Youth unemployment, averaging 1.690%, is another significant factor driving migration, particularly in cities like Kinshasa, Lagos, and Mogadishu. Food insecurity, averaging 4.990, further exacerbates migration pressures, with cities like Kinshasa and Lagos struggling to provide affordable, nutritious food.\u003c/p\u003e \u003cp\u003eLinear GMM estimation results highlight the significant impact of urban dynamics on migration. The positive coefficient for income inequality (0.9784) suggests that increasing inequality intensifies migration pressures. Similarly, the coefficient for slums (0.0029) indicates that slum prevalence contributes to higher migration rates in cities like Nairobi. The economic indicators of GDP per capita and GNI growth rate demonstrate the migrants' attraction to cities with higher economic output, such as Cape Town and Abuja, but internal inequalities often compel out-migration.\u003c/p\u003e \u003cp\u003eThese patterns are furthered in the results from nonlinear GMM estimation: the relationship of slums to migration is positive, with a coefficient of 0.419, indicating that slum populations remain the key driver of migration into cities like Kinshasa and Abidjan. This is the complex relationship that may exist between urban growth and migration, with rapid urbanization creating economic opportunities but also eventually leading to overcrowding and inequality that may force residents to look elsewhere for opportunities. Entailing this very dynamic of initial growth fostering better opportunities but once the threshold reached resource strain increases migration pressures, the coefficient for urban growth stands at \u0026minus;\u0026thinsp;1.231 while 0.032 is maintained for its squared term.\u003c/p\u003e \u003cp\u003eEconomic factors are also very important, as shown by the negative relationship between high GDP per capita (\u0026minus;\u0026thinsp;3.214) in cities such as Pretoria and Durban, and out-migration. High economic output usually means more opportunities and lower migration rates; however, in cities that experience increasing inequality, such as Luanda and Abuja, migration pressures rise due to overcrowding and limited resource availability. Income inequality remains a strong push factor, with high levels of out-migration recorded in cities like Harare and Maputo due to the widening gap in wealth.\u003c/p\u003e \u003cp\u003eAddressing urban out-migration in Sub-Saharan Africa requires policies oriented toward affordable housing, economic decentralization, and upgrading slums that reduce the cost of living and inequality. Similarly, programs dealing with youth employment and access to services such as education and healthcare are crucial. The development of infrastructure and the promotion of urban agriculture will go a long way toward improving rural-urban linkages and reducing migration pressures. Ultimately, sustainable urban growth with good governance ensures the implementation of such policies to achieve inclusive and resilient cities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNzabirinda Etienne led the study design, was responsible for drafting and revising the manuscript\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eWe are grateful to the World Bank for making the data accessible through its portal. We also thank the anonymous reviewers for their comments that have significantly enhanced this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams, S., \u0026amp; Klobodu, E. K. M. (2019). Urbanisation, economic structure, political regime, and income inequality. \u003cem\u003eSocial Indicators Research, 142\u003c/em\u003e(3), 971\u0026ndash;995.\u003c/li\u003e\n\u003cli\u003eAhrend, R., Lembcke, A. C., \u0026amp; Schumann, A. (2017). The role of urban agglomerations for economic and productivity growth. \u003cem\u003eInternational Productivity Monitor, 32\u003c/em\u003e(3), 161\u0026ndash;179.\u003c/li\u003e\n\u003cli\u003eAli, I. M. A., Attiaoui, I., Khalfaoui, R., \u0026amp; Tiwari, A. K. (2021). The impact of urbanization and industrialization on income inequality: Evidence from method of moments quantile regression. \u003cem\u003eSocial Indicators Research, 161\u003c/em\u003e(1), 29\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eAltunbaş, Y., \u0026amp; Thornton, J. (2019). The impact of financial development on income inequality: A quantile regression approach. \u003cem\u003eEconomics Letters, 175\u003c/em\u003e(3), 51\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eAlvarez, J., \u0026amp; Arellano, M. (2022). Robust likelihood estimation of dynamic panel data models. \u003cem\u003eJournal of Econometrics, 226\u003c/em\u003e(1), 21\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eAnand, S., \u0026amp; Kanbur, S. R. (1993). The Kuznets process and the inequality\u0026mdash;development relationship. \u003cem\u003eJournal of Development Economics, 40\u003c/em\u003e(1), 25\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eArellano, M., \u0026amp; Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. \u003cem\u003eThe Review of Economic Studies, 58\u003c/em\u003e(2), 277\u0026ndash;297.\u003c/li\u003e\n\u003cli\u003eArouri, M., Youssef, A. B., \u0026amp; Nguyen, C. (2017). Does urbanisation reduce rural poverty? Evidence from Vietnam. \u003cem\u003eEconomic Modelling, 60\u003c/em\u003e(1), 253\u0026ndash;270.\u003c/li\u003e\n\u003cli\u003eAsogwa, F. O., Amuka, J. I., Igwe, A. A., \u0026amp; Nkalu, C. N. (2020). Dynamics of population, urbanization, and economic growth.\u003c/li\u003e\n\u003cli\u003eAsongu, S. A., \u0026amp; Acha-Anyi, P. N. (2019). The murder epidemic: A global comparative study. \u003cem\u003eInternational Criminal Justice Review, 29\u003c/em\u003e(2), 105\u0026ndash;120.\u003c/li\u003e\n\u003cli\u003eBaltagi, B. H. (2008). Forecasting with panel data. \u003cem\u003eJournal of Forecasting, 27\u003c/em\u003e(2), 153\u0026ndash;173.\u003c/li\u003e\n\u003cli\u003eBaum-Snow, N., \u0026amp; Pavan, R. (2013). Inequality and city size. \u003cem\u003eReview of Economics and Statistics, 95\u003c/em\u003e(5), 1535\u0026ndash;1548.\u003c/li\u003e\n\u003cli\u003eBehrens, K., Duranton, G., \u0026amp; Robert-Nicoud, F. (2014). Productive cities: Sorting, selection, and aggregation. \u003cem\u003eJournal of Political Economy, 122\u003c/em\u003e(3), 507\u0026ndash;553.\u003c/li\u003e\n\u003cli\u003eBloch, R., Monroy, J., Fox, S., \u0026amp; Ojo, A. (2015). Urbanisation and urban expansion in Nigeria (URN) Research Report. London: ICF International.\u003c/li\u003e\n\u003cli\u003eBloom, D. E., Canning, D., \u0026amp; Fink, G. (2008). Urbanisation and the wealth of nations. \u003cem\u003eScience, 319\u003c/em\u003e(5864), 772\u0026ndash;775.\u003c/li\u003e\n\u003cli\u003eBlundell, R., \u0026amp; Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. \u003cem\u003eJournal of Econometrics, 87\u003c/em\u003e(1), 115\u0026ndash;143.\u003c/li\u003e\n\u003cli\u003eBond, S. R. (2002). Dynamic panel data models: A guide to micro data methods and practice. \u003cem\u003ePortuguese Economic Journal, 1\u003c/em\u003e(2), 141\u0026ndash;162.\u003c/li\u003e\n\u003cli\u003eBond, S., \u0026amp; Windmeijer, F. (2002). Finite sample inference for GMM estimators in linear panel data models. Cenmap Working Paper Series No. CWP04/02. Institute of Fiscal Studies, London.\u003c/li\u003e\n\u003cli\u003eBr\u0026uuml;lhart, M., \u0026amp; Sbergami, F. (2009). Agglomeration and growth: Cross-country evidence. \u003cem\u003eJournal of Urban Economics, 65\u003c/em\u003e(1), 48\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eCastells-Quintana, D. (2017). Malthus living in a slum: Urban concentration, infrastructure, and economic growth. \u003cem\u003eJournal of Urban Economics, 98\u003c/em\u003e, 158\u0026ndash;173.\u003c/li\u003e\n\u003cli\u003eCastells-Quintana, D. (2018). Beyond Kuznets: Inequality and the size and distribution of cities. \u003cem\u003eJournal of Regional Science, 58\u003c/em\u003e(3), 564\u0026ndash;580.\u003c/li\u003e\n\u003cli\u003eCastells-Quintana, D., Ramos, R., \u0026amp; Royuela, V. (2015). Inequality in European regions: Recent trends and determinants. \u003cem\u003eReview of Regional Research, 35\u003c/em\u003e(2), 123\u0026ndash;146.\u003c/li\u003e\n\u003cli\u003eChen, B., Liu, D., \u0026amp; Lu, M. (2017). City size, migration, and urban inequality in the People\u0026rsquo;s Republic of China (No. 723). ADBI Working Paper Series.\u003c/li\u003e\n\u003cli\u003eChristiaensen, L., \u0026amp; Weerdt, J. D. (2017). Urbanisation, growth, and poverty reduction: The role of secondary towns. Final Report. International Growth Center.\u003c/li\u003e\n\u003cli\u003eClark, R. (2013). Convergence in national income distributions. \u003cem\u003eSocial Forces, 92\u003c/em\u003e(2), 413\u0026ndash;436.\u003c/li\u003e\n\u003cli\u003eCottineau, C., Finance, O., Hatna, E., Arcaute, E., \u0026amp; Batty, M. (2019). Defining urban clusters to detect agglomeration economies. \u003cem\u003eEnvironment and Planning B: Urban Analytics and City Science\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDagum, C. (1997). Decomposition and interpretation of Gini and the generalized entropy inequality measures. \u003cem\u003eStatistica, 57\u003c/em\u003e(3), 295\u0026ndash;308.\u003c/li\u003e\n\u003cli\u003eDang, V. A., Kim, M., \u0026amp; Shin, Y. (2015). Searching robust methods for dynamic panel data models in empirical corporate finance. \u003cem\u003eJournal of Banking \u0026amp; Finance, 53\u003c/em\u003e(1), 84\u0026ndash;98.\u003c/li\u003e\n\u003cli\u003eDe Bruin, A., \u0026amp; Liu, N. (2020). The urbanization-household gender inequality nexus: Evidence from time allocation in China. \u003cem\u003eChina Economic Review, 60\u003c/em\u003e(3), 101\u0026ndash;131.\u003c/li\u003e\n\u003cli\u003eDemont, M. (2013). Reversing urban bias in African rice markets: A review of 19 national strategies. \u003cem\u003eJournal of Agricultural and Resource Economics, 38\u003c/em\u003e(3), 433\u0026ndash;453.\u003c/li\u003e\n\u003cli\u003eDorn, F., \u0026amp; Schinke, C. (2018). Top income shares in OECD countries: The role of government ideology and globalization. \u003cem\u003eThe World Economy, 41\u003c/em\u003e(9), 2491\u0026ndash;2527.\u003c/li\u003e\n\u003cli\u003eDuranton, G. (2015). Growing through cities in developing countries. \u003cem\u003eWorld Bank Research Observer, 30\u003c/em\u003e(1), 39\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eElhorst, J. P. (2014). Dynamic spatial panels: Models, methods, and inferences. In \u003cem\u003eSpatial Econometrics\u003c/em\u003e (pp. 95\u0026ndash;119). Springer, Berlin, Heidelberg.\u003c/li\u003e\n\u003cli\u003eFossaceca, A. (2019). Assessing the determinants of the human development index in oil-dependent nations. \u003cem\u003eUndergraduate Economic Review, 16\u003c/em\u003e(1), 1\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eGini, C. (1909). Concentration and dependency ratios in the population. \u003cem\u003eThe American Journal of Sociology, 15\u003c/em\u003e(2), 211\u0026ndash;224.\u003c/li\u003e\n\u003cli\u003eGuo, C., Buchmann, C. M., \u0026amp; Schwarz, N. (2019). Linking urban sprawl and income segregation: Findings from a stylized agent-based model. \u003cem\u003eEnvironment and Planning B: Urban Analytics and City Science, 46\u003c/em\u003e(3), 469\u0026ndash;489.\u003c/li\u003e\n\u003cli\u003eHa, N. M., Le, N. D., \u0026amp; Trung-Kien, P. (2019). The impact of urbanization on income inequality: A study in Vietnam. \u003cem\u003eJournal of Risk and Financial Management, 12\u003c/em\u003e(3), 146\u0026ndash;160.\u003c/li\u003e\n\u003cli\u003eHansen, L. P. (1982). Large sample properties of generalized method of moments estimators. \u003cem\u003eEconometrica: Journal of the Econometric Society, 50\u003c/em\u003e(1), 1029\u0026ndash;1054.\u003c/li\u003e\n\u003cli\u003eHardoon, D., Ayele, S., \u0026amp; Fuentes-Nieva, R. (2016). An economy for the 1%. Nairobi: Oxfam International.\u003c/li\u003e\n\u003cli\u003eHarris, J. R., \u0026amp; Todaro, M. P. (1970). Migration, unemployment, and development: A two-sector analysis. \u003cem\u003eThe American Economic Review, 60\u003c/em\u003e(1), 126\u0026ndash;142.\u003c/li\u003e\n\u003cli\u003eHausman, J. A. (1978). Specification tests in econometrics. \u003cem\u003eEconometrica, 46\u003c/em\u003e(6), 1251\u0026ndash;1271.\u003c/li\u003e\n\u003cli\u003eHeston, A., Summers, R., \u0026amp; Aten, B. (2012). Center for international comparisons of production, income, and prices at the University of Pennsylvania. \u003cem\u003ePenn World Table Version, 7\u003c/em\u003e(1).\u003c/li\u003e\n\u003cli\u003eHsiao, C., Pesaran, M. H., \u0026amp; Tahmiscioglu, A. K. (2002). Maximum likelihood estimation of fixed effects dynamic panel data models covering short periods. \u003cem\u003eJournal of Econometrics, 109\u003c/em\u003e(1), 107\u0026ndash;150.\u003c/li\u003e\n\u003cli\u003eIke, G. N., Usman, O., \u0026amp; Sarkodie, S. A. (2020). Testing the role of oil production in the environmental Kuznets curve of oil-producing countries: New insights from the method of moments quantile regression. \u003cem\u003eScience of the Total Environment, 711\u003c/em\u003e(2), 135\u0026ndash;208.\u003c/li\u003e\n\u003cli\u003eIkwuyatum, G. (2016). Migration and urbanization: Exploring the factors of the nexus in Nigeria. \u003cem\u003eInternational Journal of Humanities and Social Science, 6\u003c/em\u003e(8), 161\u0026ndash;175.\u003c/li\u003e\n\u003cli\u003eIm, K. S., Pesaran, M. H., \u0026amp; Shin, Y. (2003). Testing for unit roots in heterogeneous panels. \u003cem\u003eJournal of Econometrics, 115\u003c/em\u003e(1), 53\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eJenkins, S. P. (2015). World income inequality databases: An assessment of WIID and SWIID. \u003cem\u003eThe Journal of Economic Inequality, 13\u003c/em\u003e, 629\u0026ndash;671.\u003c/li\u003e\n\u003cli\u003eKanbur, R., \u0026amp; Zhuang, J. (2013). Urbanization and inequality in Asia. \u003cem\u003eAsian Development Review, 30\u003c/em\u003e(1), 131\u0026ndash;147.\u003c/li\u003e\n\u003cli\u003eKhan, A. U., Saboor, A., Ali, I., Malik, W. S., \u0026amp; Mahmood, K. (2016). The urbanization of multidimensional poverty: Empirical evidence from Pakistan. \u003cem\u003eQuality and Quantity, 50\u003c/em\u003e(1), 439\u0026ndash;469.\u003c/li\u003e\n\u003cli\u003eKhan, H., Khan, I., \u0026amp; Binh, T. T. (2020). The heterogeneity of renewable energy consumption, carbon emission, and financial development globally: A panel quantile regression approach. \u003cem\u003eEnergy Reports, 6\u003c/em\u003e(2), 859\u0026ndash;867.\u003c/li\u003e\n\u003cli\u003eKrugman, P. (1991). Increasing returns and economic geography. \u003cem\u003eJournal of Political Economy, 99\u003c/em\u003e(3), 483\u0026ndash;499.\u003c/li\u003e\n\u003cli\u003eKuznets, S. (1955). Economic growth and income inequality. \u003cem\u003eThe American Economic Review, 45\u003c/em\u003e(1), 1\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eLee, C. (2005). Income inequality, democracy, and public sector size. \u003cem\u003eAmerican Sociological Review, 70\u003c/em\u003e(1), 158\u0026ndash;180.\u003c/li\u003e\n\u003cli\u003eLengyel, I., \u0026amp; Szak\u0026aacute;ln\u0026eacute; Kan\u0026oacute;, I. (2012). Competitiveness of Hungarian urban micro-regions: Localization agglomeration economies and regional competitiveness function. \u003cem\u003eRegional Statistics, 2\u003c/em\u003e(1), 27\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eLevin, A., Lin, C. F., \u0026amp; Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. \u003cem\u003eJournal of Econometrics, 108\u003c/em\u003e(1), 1\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eLewis, W. A. (1954). Economic development with unlimited supplies of labor. \u003cem\u003eThe Manchester School, 22\u003c/em\u003e(5), 139\u0026ndash;191.\u003c/li\u003e\n\u003cli\u003eLi, J., Cheong, T. S., Shen, J., \u0026amp; Fu, D. (2019). Urbanisation and rural-urban consumption disparity: Evidence from China. \u003cem\u003eThe Singapore Economic Review, 64\u003c/em\u003e(4), 983\u0026ndash;996.\u003c/li\u003e\n\u003cli\u003eLi, Y., \u0026amp; Liu, X. (2018). How did urban polycentricity and dispersion affect economic productivity? A case study of 306 Chinese cities. \u003cem\u003eLandscape and Urban Planning, 173\u003c/em\u003e(2), 51\u0026ndash;59.\u003c/li\u003e\n\u003cli\u003eLi, Y., Chiu, Y., \u0026amp; Weng, W. (2020). The rise of the service sector and income inequality in China. \u003cem\u003eChina Economic Review, 59\u003c/em\u003e(1), 101\u0026ndash;132.\u003c/li\u003e\n\u003cli\u003eLiu, Z., \u0026amp; Yang, Z. (2018). Urban agglomerations and income inequality: Evidence from Chinese cities. \u003cem\u003eUrban Studies, 55\u003c/em\u003e(10), 2069\u0026ndash;2086.\u003c/li\u003e\n\u003cli\u003eLiu, C., \u0026amp; Hsieh, E. (2020). Spatial dynamics of income inequality in the developing world: The case of Southeast Asia. \u003cem\u003eGrowth and Change, 51\u003c/em\u003e(2), 616\u0026ndash;640.\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez, R. E., \u0026amp; Rodr\u0026iacute;guez, F. A. (2018). Urbanisation and income inequality: Evidence from Latin American countries. \u003cem\u003eUrban Studies, 55\u003c/em\u003e(7), 1520\u0026ndash;1538.\u003c/li\u003e\n\u003cli\u003eLutz, W., Sanderson, W., \u0026amp; Scherbov, S. (2008). The coming acceleration of global population ageing. \u003cem\u003eNature, 451\u003c/em\u003e(7179), 716\u0026ndash;719.\u003c/li\u003e\n\u003cli\u003eMaket, I. J., Kan\u0026oacute;, I. S., \u0026amp; Vas, Z. B. (2022). Reverse causal nexus between pro-poor policies and income inequality in Kenya. \u003cem\u003eRegional Science Policy \u0026amp; Practice\u003c/em\u003e, https://doi.org/10.1111/rsp3.12540.\u003c/li\u003e\n\u003cli\u003eMaket, I., Kan\u0026oacute;, I. S., \u0026amp; Vas, Z. (2023). Estimations of the pooled dynamic panel data model with time-space dependence of selected Sub-Saharan African urban agglomerations, 2000-2020. \u003cem\u003eRegional Statistics, 13\u003c/em\u003e(4), 1\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eMoretti, E. (2012). The new geography of jobs. \u003cem\u003eHoughton Mifflin Harcourt\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eNewman, P., \u0026amp; Kenworthy, J. R. (2015). The geography of sustainable development. \u003cem\u003eUrban Studies, 52\u003c/em\u003e(10), 1956\u0026ndash;1976.\u003c/li\u003e\n\u003cli\u003eNorth, D. C. (1990). Institutions, institutional change, and economic performance. \u003cem\u003eCambridge University Press.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Donnell, O., Van Doorslaer, E., \u0026amp; Wagstaff, A. (2015). Analyzing health equity using household survey data. \u003cem\u003eWorld Bank\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003ePiketty, T. (2014). Capital in the twenty-first century. \u003cem\u003eHarvard University Press\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eRavallion, M., \u0026amp; Chen, S. (2007). China\u0026apos;s (uneven) progress against poverty. \u003cem\u003eJournal of Development Economics, 82\u003c/em\u003e(1), 1\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eSachs, J. D. (2005). The end of poverty: Economic possibilities for our time. \u003cem\u003ePenguin Books\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eSen, A. (1999). Development as freedom. \u003cem\u003eOxford University Press\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eSheng, L., \u0026amp; Zeng, Q. (2021). A measure of the impact of urbanization on income inequality in China. \u003cem\u003eThe Journal of Chinese Economic and Business Studies, 19\u003c/em\u003e(4), 381\u0026ndash;399.\u003c/li\u003e\n\u003cli\u003eStern, D. I. (2017). The environmental Kuznets curve: A primer. \u003cem\u003eEnvironmental Economics and Policy Studies, 19\u003c/em\u003e(1), 51\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eTiwari, A. K., \u0026amp; Sharma, S. (2020). Impact of urbanisation and industrialisation on income inequality in developing countries. \u003cem\u003eSocial Indicators Research, 153\u003c/em\u003e(1), 1\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eYousefi, V. (2019). Inequality and its impacts on economic growth in developing countries. \u003cem\u003eEconomic Analysis and Policy, 63\u003c/em\u003e(1), 49\u0026ndash;60.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"High Living Costs, Urban Agglomeration, Inequality, Urban Out-Migration and Sub-Saharan Africa","lastPublishedDoi":"10.21203/rs.3.rs-5902961/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5902961/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper examines how high living costs, urban agglomeration, and inequality interact in Sub-Saharan African cities, focusing on the urban out-migration aspect of the spatial geo-economic politics involved. Using data from the World Bank and the GMM technique, the study identifies income inequality, rapid urbanization, and rising living costs as the main determinants of migration. The analysis reveals a net migration rate of 1.092 in large cities like Lagos, Kinshasa, and Johannesburg, driven by urban growth and economic opportunities that attract people. However, due to increasing living costs, high housing costs, and congestion, individuals are forced to move to more affordable and less crowded areas, resulting in out-migration. Income inequality, with a Gini coefficient of 0.81, where a few urban areas accumulate wealth while leaving a large portion of the population in poverty, is also linked to migration dynamics. Rapid urbanization, with an average growth rate of 4.403%, exacerbates spatial disparities and encourages migration towards peri-urban and secondary cities. The study also explores the influence of economic indicators on migration trends, using GDP per capita and GNI growth to show that economic growth and inequality play significant roles. Policy recommendations such as affordable housing, economic decentralization, slum upgrading, and improved access to services are suggested to address migration challenges and promote sustainable urban development.\u003c/p\u003e\n\u003cp\u003eJEL Classification R23, O18, D31\u003c/p\u003e","manuscriptTitle":"High Living Costs, Urban Agglomeration, and Inequality: Lessons from the Spatial Geo-Economic Politics of Urban Out-Migration in Sub-Saharan African Cities.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-07 16:17:13","doi":"10.21203/rs.3.rs-5902961/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ea6f3ed-f8a7-44f7-81e7-52f4ddfc5e3f","owner":[],"postedDate":"February 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43875533,"name":"Business and commerce/Finance"},{"id":43875534,"name":"Business and commerce/Operational research"},{"id":43875535,"name":"Scientific community and society/Geography"},{"id":43875536,"name":"Scientific community and society/Social sciences"},{"id":43875537,"name":"Social science/Economics"},{"id":43875538,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2025-05-15T05:53:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-07 16:17:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5902961","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5902961","identity":"rs-5902961","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}