Latent split of aggregate counts: revealing home deliveries per commodity types and potential freight trip implications

Abstract

This paper suggests a joint econometric model that allows estimating latent marginal counts when only total counts and types of commodities purchased are available. The basis for this model is the Negative binomial hurdle model, which is expanded by incorporating different features for the latent classes, allowing eventual null latent counts for one or more classes. A validation procedure for the proposed splitting is discussed. The methodology was used to estimate and validate a model for the propensity to shop online and the corresponding number of shipments per commodity group. The results confirm existing research on online shopping behaviour: elderly is less likely to buy online, while high income, education and having kids motivate online shopping. The average online shopper receives 2.4 shipments/month (0.077 shipments/day), with variations in shipments and commodities depending on the consumer profile. Correlation between commodity groups reveals that consolidation can reduce shipments of up to 30%.