The performance of non‐survey techniques for constructing sub‐territorial input‐output tables

Abstract

This study is a contribution to the ongoing debate on the performance of various non‐survey techniques for constructing sub‐territorial input‐output tables. Three aspects of the behaviour of the methods are analysed: performances in reproducing ‘true’ input coefficients, variability of error, and direction of bias. The analysis uses real data and in particular the world input‐output table. The most important aspect that emerges from the analysis is that even though simple location quotient (SLQ) has been identified as one of the most robust methods, its performance drops when confronted with Input Output Tables characterized by a high percentage of technical coefficients close to zero. Also the cross industry location quotient (CILQ), the semi logarithmic location quotient (RLQ), the symmetric cross industry location quotient (SCILQ) methods behave in a similar way. On the contrary, the performance of the methods the Flegg location quotient (FLQ) for δ = 0.2 and the Augmented Flegg location quotient (AFLQ) for 0.2 ≤ δ ≤ 0.3 are not affected by this situation.

The regionalization of national input‐output tables: A study of South Korean regions

“…This testimony from case studies is bolstered by the Monte Carlo simulation results of Bonfiglio and Chelli (2008) mentioned earlier. Nonetheless, some evidence to the contrary is presented by Lamonica and Chelli (2017), who find initially that the SLQ gives slightly better results than the FLQ.…”

Performance of Bidimensional Location Quotients for Constructing Input-Output Tables

“…It is therefore essential to select which LQ will be used, either alone or supplemented by adjustment techniques (Lamonica et al 2020). While there is no clear majority on which LQ yields the best results, some studies (Bonfiglio and Chelli 2008;Jahn et al 2020)show the prevalence of FLQ and AFLQ, yet others (Zhao and Choi 2015;Lamonica and Chelli 2018) oppose and favour other ratios.…”

“…Its value varies from one sub-territory to another. Multiple research papers delve into the search for the optimal value of this parameter (Kowalewksi 2015;Flegg and Tohmo 2016;Lamonica and Chelli 2018). This unknown value becomes problematic and its calculation is thus arduous (Lampiris et al 2019), probably because it is quite sensitive due to the design of the corresponding formulas.…”

“…and Smith 1974;Jackson and Murray 2004;Bonfiglio 2005;Bonfiglio and Chelli 2008;Miller and Blair 2009;Kowalewksi 2015;Wiebe and Lenzen 2016;Lamonica and Chelli 2018;Lampiris et al 2019;Lamonica et al 2020).MAPE is practically the average of STPE(Oosterhaven et al 2003;Mínguez et al 2009;Miller and Blair 2009;Lampiris et al 2019;Flegg and Tohmo 2019;Jahn et al 2020). SD-MAD is the standard deviation to the median absolute deviation between the estimated and true coefficients(Lamonica and Chelli 2018).…”

“…and Smith 1974;Jackson and Murray 2004;Bonfiglio 2005;Bonfiglio and Chelli 2008;Miller and Blair 2009;Kowalewksi 2015;Wiebe and Lenzen 2016;Lamonica and Chelli 2018;Lampiris et al 2019;Lamonica et al 2020).MAPE is practically the average of STPE(Oosterhaven et al 2003;Mínguez et al 2009;Miller and Blair 2009;Lampiris et al 2019;Flegg and Tohmo 2019;Jahn et al 2020). SD-MAD is the standard deviation to the median absolute deviation between the estimated and true coefficients(Lamonica and Chelli 2018). Theil index is known as the inequality index, as it estimates the overall distance ratio, and thus indicates perfect equality when equal to zero(Jalili 2000;Lahr and Stevens 2002;Jackson and Murray 2004;Bonfiglio 2005;Flegg and Tohmo 2013;Kowalewksi 2015;Flegg and Tohmo 2019;Lampiris et al 2019;Jahn et al 2020).…”