Graph-Based Entropy for Detecting Explanatory Signs of Changes in Market

Graph-based entropy, an index of the diversity of events in their distribution to parts of a co-occurrence graph, is proposed for detecting signs of structural changes in the data that are informative in explaining latent dynamics of consumers’ behavior. For obtaining graph-based entropy, connected sub-graphs are first obtained from the graph of co-occurrences of items in the data. Then, the distribution of items occurring in events in the data to these sub-graphs is reflected on the value of graph-based entropy. For the data on the position of sale, a change in this value is regarded as a sign of the appearance, the separation, the disappearance, or the uniting of consumers’ interests. These phenomena are regarded as the signs of dynamic changes in consumers’ behavior that may be the effects of external events and information. Experiments show that graph-based entropy outperforms baseline methods that can be used for change detection, in explaining substantial changes and their signs in consumers’ preference of items in supermarket stores.     

Age Structure and Trade Openness: An Empirical Investigation

This study focuses on the relationship between age structure and trade openness. We hypothesise that a higher share of the working‐age population in the total population increases trade openness, because the dependent population tends to spend more than the working‐age population on non‐tradable goods such as education and medical services. We estimate the effects of age structure on trade openness using panel data for 85 countries from 1991 to 2010. The empirical results show that the share of the working‐age population has a positive effect on trade openness. An increase in the share of the working‐age population is considered to be one factor that contributed to an increase in trade openness in the sample period.