ABSTRACT
Table 2 shows that the result for the relationship between urban road congestion and port throughput is consistent with our expectation. In models D1 and D2 (with large and small catchment areas, respectively), the OLS coefficients of own road congestion, ln(D), are negative and statistically significant, while the coefficients of the rival’s road congestion, ln(DR), are positive but not statistically significant. However, after using the instrumental variables for ln(D) (i.e., the natural logarithms of urban population and urban area for models DIV1-2 and the natural logarithm of urban population density for models DIV3-4) and applying the two-stage least square method (2SLS), the signs of all the coefficients remain unchanged, which are all statistically significant, and their magnitudes increased. The Durbin-Wu-Hausman tests suggest that the endogeneity issue exists, and thus the instrumental variable approach should be adopted. In models DIV1-4, we observe that the impact of rival’s road congestion is smaller than the impact of own road congestion. In particular, a 1% increase in own road congestion implies a reduction in container throughput by 0.90-2.48%, while a 1% increase in rival’s road congestion implies an increase in container throughput by 0.62-1.69%. This finding is consistent with the theoretical assumptions for demand functions: when the road congestion around a certain port (and thus the full price of using this port) increases, only a portion of the shippers who choose not to use the port will switch to the rival port indicated in the sample because the rest of them will either divert to other rival ports farther away15 or even choose not to ship the goods at all.
