A New Approach to Building the Interindustry Input--Output Table
This provides a more accurate method for constructing interindustry input-output tables, which are crucial for economic analysis and policy-making, representing an incremental improvement over existing network-based approaches.
The paper tackles the problem of estimating industry interdependence in an economy by modeling it as a latent block structure in interfirm networks, proposing an extended sparse block model that incorporates textual information and allows for an unbounded number of industries, and shows improved predictive accuracy on synthetic and real-world datasets.
We present a new approach to estimating the interdependence of industries in an economy by applying data science solutions. By exploiting interfirm buyer--seller network data, we show that the problem of estimating the interdependence of industries is similar to the problem of uncovering the latent block structure in network science literature. To estimate the underlying structure with greater accuracy, we propose an extension of the sparse block model that incorporates node textual information and an unbounded number of industries and interactions among them. The latter task is accomplished by extending the well-known Chinese restaurant process to two dimensions. Inference is based on collapsed Gibbs sampling, and the model is evaluated on both synthetic and real-world datasets. We show that the proposed model improves in predictive accuracy and successfully provides a satisfactory solution to the motivated problem. We also discuss issues that affect the future performance of this approach.