Cost of Container Shipping Delays in International Trade: A Quantile Approach

This study offers new empirical insights into the cost implications of container shipping delays and schedule unreliability on international freight rates, using a quantile regression model to capture market heterogeneity. While literature widely acknowledges the theoretical impact of schedule issues on trade costs, there has been a critical lack of empirical analysis quantifying this relationship under different market conditions. The purpose of this study is to explore the influence of schedule reliability in container transportation on freight rates. By integrating the China Containerized Freight Index (CCFI) with key variables such as average vessel delay, schedule reliability, bunker prices, and the Li Keqiang index as a proxy for demand, this research addresses that gap. Unlike conventional linear models, the use of quantile regression reveals how the effects of delays and reliability differ across various freight rate levels, yielding a U-shaped impact pattern—strongest in the worst and booming market conditions. The findings indicate that even a one-day increase in average delay can raise the CCFI by 100-226 index points, while a one-point decrease in schedule reliability can increase the index by 5-13 points. Therefore, service reliability is important for international trade, and countries, individuals, and ship owners adopt different pricing policies in response to market conditions. This study adds significant value to the field by providing policymakers and logistics stakeholders with actionable metrics on the cost of inefficiencies in global maritime transport, especially in post-pandemic and geopolitically volatile contexts.