Assessing Transport Affordability and Spatial Inequality: Evidence from a Hierarchical Bayesian Regression Framework of South Africa’s Provinces

Source abstract

Transport affordability defined as the share of household income devoted to transport expenditure is a key dimension of urban equity and social inclusion, particularly in contexts characterised by spatial inequality and income disparities. This study examines provincial variation in public transport affordability across South Africa using a hierarchical Bayesian regression framework applied to province–year data from 2015 to 2022 (n = 72). Affordability is operationalised as a transport cost burden, with higher values indicating a greater proportion of household income spent on transport, and is modelled as a function of household income, trip frequency, household population, and total provincial employment, with province-level random intercepts capturing unobserved regional heterogeneity. The results indicate that household income is negatively associated with transport cost burden, suggesting that provinces with higher average income devote a smaller share of income to transport and therefore experience better affordability. In contrast, household population and aggregate provincial employment are positively associated with transport cost burden, reflecting higher overall mobility and commuting demands in larger and more economically active provinces rather than improved affordability. Trip frequency shows no statistically meaningful association with affordability once household composition and income capacity are accounted for. After accounting for observed characteristics, between-province variation is limited, indicating that affordability dynamics are broadly similar across provinces over the study period. Methodologically, the hierarchical Bayesian framework enables partial pooling across provinces and supports probabilistic inference through credible intervals, thereby improving the stability of estimates in a small-sample multilevel context. While the analysis is associational rather than causal, the findings provide policy-relevant evidence for monitoring transport affordability, including benchmarking the prevalence of affordability burdens relative to the commonly used 10% threshold.