Estimation of regional input coefficients and output multipliers for the regions of Chile

Mardones, Cristián; Silva, Darling

doi:10.1111/pirs.12603

Cited by 2 publications

(1 citation statement)

References 29 publications

(41 reference statements)

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“…Along with other non-survey methods, the FLQ aims to offer regional analysts a means by which they can build regional tables that reflect, as closely as possible, each region's economic structure. See, for example, the application to Mexican regions by Dávila-Flores (2015) and to Chilean regions by Mardones and Silva (2021).…”

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confidence: 99%

A reformulation of the FLQ approach to computing regional input-output coefficients

Flegg,

Pereira-López,

Sánchez-Chóez

et al. 2024

Preprint

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In this paper, we examine alternative methods of computing regional input−output (IO) coefficients, with an emphasis on their relative accuracy and the complexity of the computations required. We propose a novel way of implementing the well-known FLQ (Flegg’s location quotient) approach. Although the FLQ formula often yields very satisfactory results, the need to specify values of the unknown parameter δ in this formula presents an obstacle to its implementation. Here we develop a fresh approach to the use of the FLQ that substantially simplifies its application, while simultaneously enhancing its performance. We focus on how regional size, R, is incorporated in this formula and simplify the way in which R affects the allowance made for imports from other regions. We call this new formula the reformulated FLQ or RFLQ. We also show how the unknown parameter in the RFLQ can be computed. We test our proposal using the 2005 and 2015 Korean survey-based interregional IO datasets and contrast our estimates with both survey-based values and the results from several other techniques. We also examine two different information scenarios: with and without industry-specific information. The results suggest that the RFLQ can yield more accurate estimates of regional IO coefficients, and in a more straightforward way, than is possible with the traditional FLQ.

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confidence: 99%