A New Approach to Building the Interindustry Input--Output Table

TL;DR

This paper introduces a novel data science-based method for estimating interindustry dependencies using network data, extending existing models with textual information and a flexible industry interaction framework.

Contribution

It proposes an extended sparse block model with a two-dimensional Chinese restaurant process to better capture industry interdependence from buyer-seller networks.

Findings

Improved predictive accuracy over existing models

Effective modeling of industry interactions with textual data

Successful validation on synthetic and real datasets

Abstract

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.