南非的空间不平等：原因和政策选择

Spatial Inequality in SouthAfrica: Causes and Policy Sergii Meleshchuk and Johanna Schauer SIP/2026/018 IMF Selected Issues Papers are prepared by IMF staff asbackground documentation for periodic consultations withmember countries.It is based on the information available atthe time it was completed on January 21, 2026. This paper is 2026MAR IMF Selected Issues PaperAfrican DepartmentSpatial Inequality in South Africa: Causes and Policy OptionsPrepared by Sergii Meleshchuk and Johanna SchauerAuthorized for distribution by Delia VelculescuMarch2026 IMF Selected Issues Papersare prepared by IMF staff as background documentation for periodicconsultations with member countries.It is based on the information available at the time it was ABSTRACT:South Africa exhibits one of the highest levels of income inequality globally, reflecting persistentspatial exclusion. This paper examines the extent and causes of spatial inequality using household microdata,microsimulations, and a structural spatial general-equilibrium model. The microdata analysis indicates thatinequality within (rather than across) urban and rural areas accounts for the majority of overall inequality, with RECOMMENDED CITATION:Meleshchuk, Sergii, and Schauer, Johanna. 2026. Spatial Inequality in SouthAfrica: Causes and Policy Options. IMF Selected Issue Paper No. 26/018 SELECTED ISSUES PAPERS Spatial Inequility in South Africa:Causes and Policy Options South Africa Prepared by Sergii Meleshchuk and Johanna Schauer A. Introduction 1.South Africa’s inequality is one of the highest in the world.Despite significant fiscal redistribution,South Africa’s Gini coefficient, estimated at about 0.65, points to one of the most unequal market incomes inthe world (Figure 1, World Bank, 2018; Stats SA, 2023). The Theil index (described in Box 1) paints a similarpicture. The literature highlights a number of drivers of inequality, including weak growth, product andlabor-market rigidities (IMF 2024, OECD 2022, Nattrass & Seekings, 2019), premature deindustrialization, deprived of access to quality education, skilledemployment, and productive assets (HarvardGrowth Lab, 2023), this resulted in a largeunderprivileged population with low initial incomesand limited opportunities, laying the foundation for 2.This paper focuses on spatialinequality as a critical determinant of overallincome inequality in South Africa.As noted Black South Africans from access to infrastructure, education, and labor market opportunities. In rural regions, the creation of under-resourced “homelands”confined populations to areas far fromeconomically productive centers, perpetuatingchronic poverty (World Bank, 2018; HarvardGrowth Lab, 2023). The establishment ofperipheral townships entrenched the physical andeconomic isolation of urban Black communities, Source: Shah and Sturzenegger (2022). The bars depict averagetotal transport cost (monetary and non-monetary) as a share oflabor income by quintile to household per capita income. employment centers (Shah and Sturzenegger, 2022). Commuting is particularly burdensome: transport aloneconsumes about 17 percent of wages, rising to over 30–40 percent when accounting for time lost (Kerr, 2015).For public transit users, total commuting costs can reach 80 percent of net income, discouraging employment(Figure 2, Shah and Sturzenegger, 2022) and leading to a paradox of high unemployment and a 3.The paper combines microdata, microsimulations, and structural modeling to estimate theextent of spatial inequality in South Africa and analyze policy options that could help address it. Leveraging nationally representative data from the 2024 General Household Survey, the analysis quantifieshow disparities in geographic access to economic opportunities contribute to persistent income andemployment gaps across urban and rural areas (section II). A microsimulation framework (similar toBourguignon and Spadaro, 2006) imposes exogenous employment shocks on household-level data to trace B. Microdata Analysis of Spatial Inequality 4.This analysis draws on the 2024 South Africa General Household Survey (GHS), to assessspatial inequality in South Africa.This nationally representative dataset captures household- and individual-level data on demographic and socioeconomic characteristics, including province, metropolitan status,educational attainment, labor income, and access to basic services. Importantly, it also includes spatialindicators such as distance to the nearest transport facility, enabling an examination of geographic andstructural drivers of inequality. These features make the GHS a critical source for analyzing both distributional 5.Spatial income3inequality in SouthAfrica is driven primarily by disparitieswithin urban and rural areas rather thandifferences between them.According to theGHS data (Figure 3), inequality within ruralareas accounts for about 57 percent of overallincome inequality, while inequality within 6.Spatial exclusion i