Revealing production networks from firm growth dynamics

Abstract

We study the correlation structure of firm growth rates. We show that most firms are correlated because of their exposure to a common factor but that firms linked through the supply chain exhibit a stronger correlation on average than firms that are not. Removing this common factor significantly reduces the average correlation between two firms with no relationship in the supply chain while maintaining a significant correlation between two firms that are linked. We then demonstrate how this observation can be used to reconstruct the topology of a supply chain network using Gaussian Markov Models.

Reconstructing supply networks

“…They spread negative or positive shocks from one firm to its customers and suppliers, generating correlations between firms' fundamentals, such as market valuation and sales [58][59][60]. Starting from this observation and leveraging the graph learning literature, Mungo and Moran [42] introduce a method to reconstruct the production network from the time series of firm sales, s i (t). First, the authors show empirically that the correlation between the log-growth rates of firms connected in the production network surpasses the average correlation yielded by randomly sampled firm pairs, and this excess correlation decreases as firms get further apart in the supply chain.…”

“…A related question for further research will be to establish the potential of 'dynamical' data. Mungo and Moran [42] showed that while there is information about the network in the sales growth rates correlation matrix, predicting the network remains difficult, as the distribution of pairwise correlation for connected and unconnected pairs overlaps greatly, even though their average is statistically significantly different. Nevertheless, there are interesting developments in this area for networks generally, with only one application to supply networks.…”