Shocks to Supply Chain Networks and Firm Dynamics: An Application of Double Machine Learning

We examine the association between changes in supply chain networks and firm dynamics. To determine the causal relationship, first, using data on over a million Japanese firms, we construct machine learning-based prediction models for the three modes of firm exit (i.e., default, voluntary closure, and dissolution) and firm sales growth. Given the high performance in those prediction models, second, we use the double machine learning method (Chernozhukov et al. 2018) to determine causal relationships running from the changes in supply chain networks to those indexes of firm dynamics. The estimated nuisance parameters suggest, first, that an increase in global and local centrality indexes results in lower probability of exits. Second, higher meso-scale centrality leads to higher probability of exits. Third, we also confirm the positive association of global and local centrality indexes with sales growth as well as the negative association of a meso-scale centrality index with sales growth. Fourth, somewhat surprisingly, we found that an increase in one type of local centrality index shows a negative association with sales growth. These results reconfirm the already reported correlation between the centrality of firms in supply chain networks and firm dynamics in a causal relationship and further show the unique role of centralities measured in local and medium-sized clusters.