Automation and Container Throughput: Heterogeneous Evidence from a Staggered Difference-in-Differences Design

Fully automated container terminals promised step changes in throughput, yet the empirical record rests on engineering simulations and cross-sectional efficiency scores that do not identify the causal effect of adoption. We provide an exploratory causal assessment of port-level throughput after automation, applying the Callaway–Sant'Anna staggered difference-in-differences estimator to a port-year panel of 56 container ports over 2001–2024 and seven adoption cohorts. Synthetic control, changes-in-changes, and a size-matched placebo provide complementary checks. The pooled average treatment effect on log throughput is +0.112 (95% uniform confidence band [−0.015, 0.240]). One atypical cohort (Abu Dhabi 2020) is the largest single influence on the pooled estimate; its weight in the aggregate is limited by its short post-adoption window. Confidence bands for five of the remaining six cohorts include zero, and the Abu Dhabi estimate is identification-fragile under donor-pool and Oster-bound diagnostics. The size-matched placebo returns an ATT of +0.075 [−0.114, 0.263], so the treated-port estimate is not statistically distinguishable from placebo variation. A dose-response specification at two dual-terminal port-clusters (Rotterdam, San Pedro Bay) is consistent with saturation rather than additive gains. The evidence reframes the port-level throughput response to automation as context-conditional rather than uniform, with implications for infrastructure lending and terminal-investment appraisal.