Anthony T. Flegg

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

This article examines the effectiveness of a new non-survey regionalization method: Kronenberg’s Cross-Hauling Adjusted Regionalization Method (CHARM). This aims to take into account the fact that regions typically both import and export most commodities. Data for Uusimaa, Finland’s largest region, are employed to carry out a detailed empirical test of CHARM. This test gives very encouraging results. CHARM is suitable for studying environmental questions, but it can only be applied in situations where foreign imports have been included in the national input–output table. Where the focus is on regional output and employment, location quotients (LQs) can be used for purposes of regionalization. On both theoretical and empirical grounds, the FLQ appears to be the most suitable LQ currently available. It should be applied to national input–output tables that exclude foreign imports. Both types of table are available at the national level for all European Union members as well as for some other countries.

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

Flegg A. T. and Tohmo T. Estimating regional input coefficients and multipliers: the use of FLQ is not a gamble, Regional Studies. This paper re-examines the Finnish evidence presented by Lehtonen and Tykkyläinen on the use of location quotients (LQs) in estimating regional input coefficients and multipliers. They argue that the choice of an LQ-based method is a gamble and that there is no single method that can be recommended for general use. It is contended here that this evidence is erroneous and that the FLQ (Fleggs location quotient) yields results far superior to those from competing formulae, so it should provide a satisfactory way of generating an initial set of input coefficients. The choice of a value for the parameter δ is also examined.

Evaluating the FLQ and AFLQ formulae for estimating regional input coefficients: empirical evidence for the province of Córdoba, Argentina

ABSTRACT This paper uses survey-based data of the Argentinian province of Córdoba to conduct an empirical test of the performance of the Fleggs location quotient (FLQ) and augmented FLQ (AFLQ) formulae for estimating regional input coefficients. A comparison is made with conventional methods based on location quotients. The possibility of using prior information about the extent of self-sufficiency of particular sectors is explored. The empirical work employs a range of statistical criteria with contrasting properties, and examines performance in terms of each methods ability to estimate regional input coefficients, output multipliers and imports. Particular attention is paid to the problem of choosing a value for the unknown parameter δ in the FLQ and AFLQ formulae. These formulae are found to give the best overall results of the non-survey methods considered in the paper. However, the AFLQ typically produces slightly more accurate results than the FLQ, in line with the findings of previous studies.

Using CHARM to adjust for cross-hauling: the case of the Province of Hubei, China

Data for the Chinese province of Hubei are used to assess the performance of Kronenbergs Cross-Hauling Adjusted Regionalization Method (CHARM), a method that takes explicit account of cross-hauling when constructing regional input–output tables. A key determinant of cross-hauling is held to be the heterogeneity of commodities, which is estimated using national data. However, contrary to the authors’ findings for Finland, CHARM does not generate reliable estimates of Hubeis sectoral exports, imports and volume of trade, although it is more successful in estimating sectoral supply multipliers. The poor simulations of regional trade are attributed to the fact that Hubei is a relatively small region, where there is a large divergence between regional and national technology and pattern of final demand. The simulation errors are decomposed into components reflecting differences between regional and national technology, final demand and heterogeneity. The third component is found to be the least important of the three sources of error.