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This study examines whether Islamic banks in SSA exhibit convergence in operational efficiency or whether performance disparities persist over time. Specifically, it evaluates whether less efficient banks catch up with more efficient peers within the region’s emerging Islamic banking sector. The study adopts a two-stage empirical framework using panel data from 35 Islamic banks across SSA over the period 2010–2024. In the first stage, operational efficiency scores are estimated using a bias-corrected Data Envelopment Analysis (DEA) model following the Simar and Wilson two-stage approach. An input-oriented specification under Variable Returns to Scale (VRS) is employed to reflect cost minimization behaviour and heterogeneity in bank size. Bias correction is implemented using a bootstrap procedure to obtain consistent efficiency estimates. In the second stage, convergence dynamics are analysed using sigma (σ) and beta (β) convergence models, alongside conditional convergence regressions incorporating bank size, age, and market concentration. The results reveal significant β-convergence, with the baseline model yielding a coefficient of −0.267 (p < 0.01), while the conditional model confirms robust convergence (β = −0.2836, p < 0.01), indicating that banks with lower initial efficiency improve at a faster rate than more efficient institutions, consistent with catch-up dynamics. However, σ-convergence results show that efficiency dispersion declined between 2010 and 2019 but increased after 2020, indicating that convergence was time-varying rather than uniform. This suggests that while convergence forces exist, structural differences and external shocks continue to sustain efficiency gaps across banks. The findings highlight the need for stronger regulatory harmonization, improved financial infrastructure, and targeted capacity-building initiatives to accelerate efficiency convergence across Islamic banks in SSA.

This study investigates whether inefficiencies in Islamic banking in Sub-Saharan Africa (SSA) are primarily driven by managerial limitations or by suboptimal scale of operations. While existing studies largely report aggregate efficiency scores, limited attention has been given to decomposing efficiency into its underlying components in emerging Islamic banking systems, particularly within the SSA context. The study employs a balanced panel of 35 fully fledged Islamic banks operating in SSA over the period 2010–2024. Operational efficiency is estimated using a bias-corrected Data Envelopment Analysis (DEA) model under an input-oriented Variable Returns to Scale (VRS) framework. To enhance statistical reliability, the Simar–Wilson bootstrap procedure with 2,000 replications is applied. Efficiency scores are decomposed into pure technical efficiency and scale efficiency to distinguish between managerial inefficiencies and structural scale constraints. The results indicate that overall efficiency levels remain low, with inefficiencies largely driven by scale factors rather than managerial performance. A significant proportion of banks operate under increasing returns to scale, suggesting suboptimal size linked to structural constraints such as limited market depth, fragmented regulatory environments, and underdeveloped financial infrastructure. Although pure technical efficiency shows moderate improvement over time, managerial gains are insufficient to offset these systemic limitations. The findings highlight the need for regulatory harmonization, market integration, and strategic expansion or consolidation to enable Islamic banks to achieve optimal scale and improve operational efficiency in SSA. This study provides one of the first comprehensive efficiency decomposition analyses of Islamic banks in SSA using bias-corrected DEA, offering new insights into the relative importance of structural versus managerial sources of inefficiency in emerging financial systems.

This study examines whether Islamic banks in SSA exhibit convergence in operational efficiency or whether performance disparities persist over time. Specifically, it evaluates whether less efficient banks catch up with more efficient peers within the region’s emerging Islamic banking sector. The study adopts a two-stage empirical framework using panel data from 35 Islamic banks across SSA over the period 2010–2024. In the first stage, operational efficiency scores are estimated using a bias-corrected Data Envelopment Analysis (DEA) model following the Simar and Wilson two-stage approach. An input-oriented specification under Variable Returns to Scale (VRS) is employed to reflect cost minimization behaviour and heterogeneity in bank size. Bias correction is implemented using a bootstrap procedure to obtain consistent efficiency estimates. In the second stage, convergence dynamics are analysed using sigma (σ) and beta (β) convergence models, alongside conditional convergence regressions incorporating bank size, age, and market concentration. The results reveal significant β-convergence, with the baseline model yielding a coefficient of −0.267 (p < 0.01), while the conditional model confirms robust convergence (β = −0.2836, p < 0.01), indicating that banks with lower initial efficiency improve at a faster rate than more efficient institutions, consistent with catch-up dynamics. However, σ-convergence results show that efficiency dispersion declined between 2010 and 2019 but increased after 2020, indicating that convergence was time-varying rather than uniform. This suggests that while convergence forces exist, structural differences and external shocks continue to sustain efficiency gaps across banks. The findings highlight the need for stronger regulatory harmonization, improved financial infrastructure, and targeted capacity-building initiatives to accelerate efficiency convergence across Islamic banks in SSA.

This study investigates whether inefficiencies in Islamic banking in Sub-Saharan Africa (SSA) are primarily driven by managerial limitations or by suboptimal scale of operations. While existing studies largely report aggregate efficiency scores, limited attention has been given to decomposing efficiency into its underlying components in emerging Islamic banking systems, particularly within the SSA context. The study employs a balanced panel of 35 fully fledged Islamic banks operating in SSA over the period 2010–2024. Operational efficiency is estimated using a bias-corrected Data Envelopment Analysis (DEA) model under an input-oriented Variable Returns to Scale (VRS) framework. To enhance statistical reliability, the Simar–Wilson bootstrap procedure with 2,000 replications is applied. Efficiency scores are decomposed into pure technical efficiency and scale efficiency to distinguish between managerial inefficiencies and structural scale constraints. The results indicate that overall efficiency levels remain low, with inefficiencies largely driven by scale factors rather than managerial performance. A significant proportion of banks operate under increasing returns to scale, suggesting suboptimal size linked to structural constraints such as limited market depth, fragmented regulatory environments, and underdeveloped financial infrastructure. Although pure technical efficiency shows moderate improvement over time, managerial gains are insufficient to offset these systemic limitations. The findings highlight the need for regulatory harmonization, market integration, and strategic expansion or consolidation to enable Islamic banks to achieve optimal scale and improve operational efficiency in SSA. This study provides one of the first comprehensive efficiency decomposition analyses of Islamic banks in SSA using bias-corrected DEA, offering new insights into the relative importance of structural versus managerial sources of inefficiency in emerging financial systems.