Estimating the agglomeration benefits of transport investments: some tests for stability

The case for including agglomeration benefits within transport appraisal rests on an assumed causality between access to economic mass and productivity. Such causality is justified by the theory of agglomeration, but is difficult to establish empirically because estimates may be subject to sources of bias from endogeneity and confounding. The paper shows that conventional panel methods used to address these problems are unreliable due to the highly persistent nature of accessibility measures. Adopting an alternative approach, by applying semiparametric techniques to restricted sub-samples of the data, we find considerable nonlinearity in the relationship between accessibility and productivity with no positive effect to be discerned over broad ranges of the data. A key conclusion is that we are unable to distinguish the role of accessibility from other potential explanations for productivity increases. For transport appraisal, this implies that the use of conventional point elasticity estimates could be highly misleading.     

On the estimation of trip matrices in the case of missing and uncertain data

The problem of missing data in the base (calibration) year is of major importance when applying the gravity model in transportation planning, because base year data are used for calibration of the model. In this article, this problem is tackled by solving an optimization problem for the prediction year for values of the entropy level and the proportions of trips in a given stratification of the cells of the trip matrix estimated in the base year. The problem of uncertain data in the prediction year is handled by replacing equality constraints for the marginal totals in the prediction year by interval constraints. Computational results are given for trip data from the Linköping area.