Estimating Regional Input Coefficients and Multipliers: The Use of FLQ is Not a Gamble

Flegg, Anthony T.; Tohmo, Timo

doi:10.1080/00343404.2014.901499

Cited by 52 publications

(35 citation statements)

References 15 publications

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“…As expected, the FLQ outperforms the SLQ and CILQ but the extent of this superior performance is striking. It echoes the clear‐cut findings in the Monte Carlo study of Bonfiglio and Chelli (), yet other authors such as Flegg and Tohmo (, ), Flegg et al () and Kowalewski () have found more modest differences in performance. An interesting facet of the results is that MAPE is minimized at a relatively high value of δ in both regions.…”

Section: Zhao and Choi's Studysupporting

confidence: 83%

“…The first step in the application of LQs is to transform the national transactions matrix into a matrix of input coefficients. This matrix can then be ‘regionalized’ via the formula:

r_{ij} = β_{ij} \times a_{ij},

where r ij is the regional input coefficient, β ij is an adjustment coefficient and a ij is the national input coefficient (Flegg & Tohmo, ). r ij measures the amount of regional input i required to yield one unit of regional gross output j ; it thus excludes any supplies of i from other domestic regions or from abroad.…”

Section: The Flq and Related Formulaementioning

confidence: 99%

“…It is now well established that the FLQ can give more precise results than the SLQ and CILQ. This evidence includes, for instance, case studies of Scotland (Flegg & Webber, ), Finland (Flegg & Tohmo, , ; Tohmo, ), Germany (Kowalewski, ), Argentina (Flegg et al, ) and Ireland (Morrissey, ). This testimony from case studies is bolstered by the Monte Carlo simulation results of Bonfiglio and Chelli () mentioned earlier.…”

Section: The Flq and Related Formulaementioning

confidence: 99%

“…It is desirable, therefore, to employ a range of criteria with different properties. In line with previous research (Flegg et al, ; Flegg & Tohmo, , ), the following additional statistics will be employed to evaluate the estimated multipliers:

normalMPE = ((), 100 / 28) \sum_{j} ((), {\overset{truê}{m}}_{j} - m_{j}) / m_{j},

WMPE = 100 \sum_{j} w_{j} ((), {\overset{truê}{m}}_{j} - m_{j}) / m_{j},

normalS = {\{sd ({\overset{truê}{m}}_{j}) - sd (m_{j})\}}^{2},

U = 100 \sqrt{\frac{\sum_{j} {((), {\overset{truê}{m}}_{j} - m_{j})}^{2}}{\sum_{j} {m_{j}}^{2}}} .

…”

Section: Extension To All Regionsmentioning

confidence: 99%

“…where r ij is the regional input coefficient, β ij is an adjustment coefficient and a ij is the national input coefficient (Flegg & Tohmo, 2016). r ij measures the amount of regional input i required to yield one unit of regional gross output j; it thus excludes any supplies of i from other domestic regions or from abroad.…”

Section: The Flq and Related Formulaementioning

confidence: 99%

See 4 more Smart Citations

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

Flegg

Tohmo

2019

Papers in Regional Science

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This paper uses survey‐based data for 16 South Korean regions to refine the application of Flegg's location quotient (FLQ) and its variant, the sector‐specific FLQ (SFLQ). These regions vary markedly in terms of size. Especial attention is paid to the problem of choosing appropriate values for the unknown parameter δ in these formulae. Alternative approaches to this problem are evaluated and tested. Our paper adds to earlier research that aims to find a cost‐effective way of adapting national coefficients, so as to produce a satisfactory initial set of regional input coefficients for regions where survey‐based data are unavailable.

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Section: Zhao and Choi's Studysupporting

confidence: 83%

“…The first step in the application of LQs is to transform the national transactions matrix into a matrix of input coefficients. This matrix can then be ‘regionalized’ via the formula: