Regional Size, Regional Specialization and the FLQ Formula

Abstract

FLEGG A. T. and WEBBER C. D. (2000) Regional size, regional specialization and the FLQ formula, Reg. Studies 34 , 563- 569. In examining how regional size might affect regional input-output coefficients, MCCANN and DEWHURST, 1998, raise some concerns about our FLQ formula for estimating such coefficients from national data. In particular, they point to the need to consider regional specialization when modelling regional economies. However, our empirical work using Scottish data shows that the inclusion of a measure of regional specialization in LQ-based formulae does not yield more accurate estimates of regional coefficients. We find too that the FLQ invariably outperforms its main rivals, the SLQ and CILQ. FLEGG A. T. et WEBBER C. D. (2000) La taille des régions, la spécialisation régionale et la formule FLQ, Reg. Studies 34 , 563-569. En examinant comment la taille de la région pourrait in uer sur les coefficients d'échanges interindustriels, McCann et Dewhurst, 1998, font soulever quelques soucis quant à notre formule FLQ qui cherche à estimer de tels coefficients à partir des données nationales. En particulier, ils semblent indiquer que la modélisation des économies régionales nécessite un examen de la spécialisation régionale. Cependant, notre recherche empirique, qui emploie des données pour l'Ecosse, montre que les formules LQ qui comportent une mesure de la spécialisation régionale ne fournissent pas d'estimations plus exactes des coefficients régionaux. Il s'avère aussi que la formule FLQ fournit de meilleurs résultats par rapport à ses principales alternatives, à savoir les formules SLQ et CILQ. FLEGG A. T. und WEBBER C. D. (2000) Regionalgröße, regionale Spezialisierung und die FLQ Formel, Reg. Studies 34 , 563‐569. Bei der Untersuchung der Auswirkung der Größe einer Region auf Aufwand-Ertragskoeffizienten äußern McCann and Dewhurst im Jahre 1998 Bedenken über die FLQ Formel des Autors zur Schätzung solcher Koeffizienten von Daten, die das ganze Land betreffen. Vorallem weisen sie auf die Notwendigkeit hin, regionale Spezialisierung beim Entwurf von regionalen Wirtschaften zu berücksichtigen. Die empirische Arbeit mit schottischen Daten zeigt jedoch, daß die Einbeziehung eines gewissen Maßes regionaler Spezialisierung in LQ gestützten Formeln keine genaueren Schätzungen regionaler Koeffizienten ergibt. Die Autoren stellen auch fest, daß die FLQ ihren Hauptrivalen, der SLQ und der CILQ, ausnahmslos überlegen ist.Input-OUTPUT, Location Quotients, Regional Models,

A Comment on Tobias Kronenberg’s “Construction of Regional Input-Output Tables Using Nonsurvey Methods: The Role of Cross-Hauling”

“…Such cases are catered for by the augmented FLQ (AFLQ), which includes a regional specialization term. However, the empirical evidence suggests that this more complex adjustment formula does not yield significantly better results (Flegg and Webber, 2000;Bonfiglio and Chelli, 2008;Flegg and Tohmo, 2011).…”

“…This evidence includes studies using survey-based data for Peterborough (Flegg et al, 1995), Scotland (Flegg and Webber, 2000), one Finnish region (Tohmo, 2004), all Finnish regions (Flegg and Tohmo, 2011), along with the Monte Carlo study by Bonfiglio and Chelli (2008), who examined 400,000 sectoral output multipliers. On the other hand, Riddington et al (2006) found the FLQ to be unhelpful, albeit on the basis of findings pertaining to a single sector in one Scottish region (Flegg and Tohmo, 2011).…”

City Carbon Footprint Networks

“…This is achieved by utilising locally (SA2) specific business turnover data and employment and income data from the latest census to disaggregate national and state-level input-output and GHG data [49]. Flegg's adjusted location quotient method [50,51] is employed as a non-survey method and a modified RAS method is used for re-balancing the MRIO table [39,52]. Three GHGs were considered for the present analysis: carbon dioxide, methane and nitrous oxide.…”

Methods for regionalizing input-output tables

“…Thus, the optimal range of δ can be determined by reviewing the literature. Based on studies of Flegg and Tohmo (2013) in Finland, Kowalewski (2012) in Germany, Flegg and Webber (2000) in Scotland and Bonofiglio and Chelli (2008), we can state that δ is between 0.1 and 0.3.…”