A new approach to modelling the input\u2013output structure of regional economies using non-survey methods

Anthony T Flegg,Maria C Recchioni,Francesco M Chelli,Timo Tohmo,Guiseppe R Lamonica

doi:10.1186/s40008-021-00242-8

Abstract

This paper proposes a new approach to the regionalization of national input–output tables where suitable regional data are scarce and analysts are considering using location quotients (LQs). We focus on the FLQ formula, which frequently yields the best results of the pure LQ-based methods, and develop an enhanced way of implementing this approach. We use a modified cross-entropy (MCE) method, along with a regression model, to estimate values of the unknown parameter δ in the FLQ formula, specific to both region and country. An analysis of survey-based data for 16 South Korean regions reveals that the proposed FLQ+ approach yields more accurate estimates of both input coefficients and sectoral output multipliers than those from simpler LQ-based methods or the MCE approach alone. Sectoral outputs (or employment) are the only regional data required. The MCE method also clearly outperforms GRAS.

Highlights

Many non-survey methods of regionalizing a national input–output table (NIOT) have been developed, with the aim of avoiding the high costs and lengthy delays associated with constructing regional tables via survey-based methods
In order to demonstrate the practical advantages of using the FLQ+ method, we compare its performance with the results from the modified cross-entropy (MCE) method, GRAS and the FLQ with a single assumed value of δ for every region
8 Conclusion This paper has proposed a new approach to the regionalization of national input–output tables where very limited regional data exist and analysts are considering employing methods based on location quotients

Summary

Introduction

Many non-survey methods of regionalizing a national input–output table (NIOT) have been developed, with the aim of avoiding the high costs and lengthy delays associated with constructing regional tables via survey-based methods. This strategy combines constrained matrix-balancing procedures with the FLQ. Where such data are unavailable, sectoral employment could be used as a proxy This proposed hybrid method, hereafter referred to as the FLQ+ method, is an improvement on the present state of the art, in which analysts need to select values of δ on the basis of a priori considerations, e.g. values found in earlier studies, or by taking regional characteristics into account, as is suggested by Jahn et al (2020). In order to demonstrate the practical advantages of using the FLQ+ method, we compare its performance with the results from the MCE method, GRAS and the FLQ with a single assumed value of δ for every region

Review of pure LQ‐based methods

Analysis of input coefficients

Findings

Conclusion