南非的空间不平等：原因和政策选择（英）

Spatial Inequality in SouthAfrica: Causes and PolicyOptions 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 isalso published separately as IMF Country Report No 26/35 2026MAR IMF Selected Issues PaperAfrican Department Spatial Inequality in South Africa: Causes and Policy OptionsPrepared by Sergii Meleshchuk and Johanna Schauer Authorized 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 wascompleted on January 21, 2026. This paper is also published separately as IMF Country Report No 26/35 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, withlong commuting times strongly associated with higher unemployment and lower incomes. The simulationanalysis suggests that reducing commuting costs can meaningfully lower unemployment and inequality. Thestructural modeling results point to transportation and housing policies as effective tools to promote spatialintegration and reduce income inequality. Spatial Inequility in South Africa:Causes and Policy Options South Africa Prepared by Sergii Meleshchuk and Johanna Schauer1 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,skills-biased technological change, and high unemployment (Rodrick 2008). However, what sets South Africaapart from other countries is thedeep racial and geographical disparities that are legacies of the apartheid era.As the Black majority was geographically confined to economically marginal territories and systematically 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 forpersistently high spatially-driven inequality– i.e. systematic disparities in economic andsocial outcomes across geographic areas withinthe country (Manysheva and others, 2025). 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,resulting in high commuting costs, fragmentedlabor markets, and constrained access to qualityeducation (Manysheva and others, 2025; HarvardGrowth Lab, 2023). Despite policy efforts toaddress these issues in the post-apartheid period,low-income populations remain situated far from 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 asmall informal sector (Rodrik, 2008; Shah and Sturzenegger, 2022, Harvard Growth Lab, 2023).2In rural areas, poor connectivity further limits opportunities. 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 tracethe distributional effects of improved labor-market access (section III). Finally, a structural spatialgeneral-equilibrium model develo