Efficiency, Queueing Pressure, and Input Slack in Major Global Container Ports: A Double-Bootstrap DEA Diagnostic for Resilience-Oriented Port Assessment

High measured efficiency can indicate a well-run container port or one operating with little spare capacity; an efficiency score alone does not distinguish the two. This study examines port efficiency after finite-sample bias correction and alongside evidence of queueing pressure and phase-II normalized input slack measured relative to the estimated frontier. Using double-bootstrap data envelopment analysis for 18 major global container ports, with annual container throughput as output, we estimate bias-corrected efficiency and its conditional association with two AIS-derived indicators: berthing time per TEU and waiting time per vessel. In the baseline specification, longer berthing time per TEU is significantly associated with greater input inefficiency, whereas waiting time is not significant, indicating that efficiency and congestion capture distinct operational dimensions. This service-time association remains positive but loses statistical significance when berth time is normalized by called vessel capacity rather than by throughput, so it is reported as sensitive to the choice of normalization. Joint analysis with phase-II maximal normalized input slack identifies high-efficiency ports with substantial queues despite zero measured maximal normalized slack and lower-efficiency ports where input excess coexists with congestion. These findings show that neither efficiency nor infrastructure slack alone identifies operating headroom for absorbing disruption. The study provides bias-corrected efficiency measurement and bootstrap inference, distinguishes berth-side service productivity from queueing pressure, and offers a static operational diagnostic, scalable across ports, that characterizes operating conditions relevant to absorptive capacity without treating efficiency or congestion as resilience itself.