Regions and industries are not isolated islands; so, when evaluating productivity growth, regional and sectoral growth paths should not be expected to generate independently. Moreover, accounting for spatial interactions via econometric models has become normal practice; but modelling interindustry dependencies has not. Thus, we expand labour productivity econometric convergence models by introducing interindustry spillovers in addition to spillovers that are spatial in nature. To illustrate our findings, we present an empirical application predicated upon Galicia (extreme northwest Spain), a region posing major challenges to such modelling. Our results point to the relevance of interindustry spillovers in explaining productivity growth. Furthermore, the approach allows us to better interpret covariates that explain the different growth paths across regions and industries, thus enabling more reliable policy recommendations. We find that interindustry dependencies transmit productivity shocks across regions. In addition, our results suggest that spatial and interindustry dependencies should be considered when formulating (sub)regional economic development policies. Finally, our approach corrects possible mis-specification problems that arise from data scarcity. This makes it a viable alternative for multiregional econometric tests in which some sectoral detail is needed. It is particularly useful for sets of regions where data needed to populate such models is scarce.

A basic underlying assumption in most of the research to date is that intermediate industry accounts of the economies in multiregional input-output (MRIO) tables exist and are accurate. In fact, if they exist at the subnational level, such accounts are, at best, roughly estimated and predicated on far less empirical information than is available for economies of nations. Moreover, intra-economy intermediate-industry flows are typically larger than the set of a region's commodity in- and out-flows. So, if intermediate industry flows in a set of MRIO accounts are noticeably mis-estimated, it follows that interregional trade coincidentally derived using them must be even more conspicuously in error. We hypothesize as more information is used to estimate MRIO accounts, the better the estimates should be. We start our experiment by consolidating 2019 FIGARO accounts of the 27 member states of the European Union, while maintaining sectoral detail, to produce a “national account”. We then test several approaches to constructing MRIO tables. The approaches distribute interregional trade fully by receiving industry, as in FIGARO, as well as strictly in the form of a diagonalized matrix as if the commodity inflows are competitive imports. To do this, both a gravity model and RAS are applied to each approach. We then test to see how well the approaches estimate main features of FIGARO's MRIO accounts and detail a rather consistent ranking of the relative accuracy of them. We also find that the level of error inherent to the estimated MRIOs is markedly similar across approaches, particularly for multipliers. Further, relaxing interregional trade to a diagonalized matrix tends to add very little error. The approach that uses the least data is, however, markedly worse in replicating countries’ direct requirements matrices and Leontief inverses, which suggests its use in a more-limited set of applications.

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.

Regional input-output analysis is a widely used tool for regional scientists to study economic, social and environmental phenomena. Ever since its first steps, regional input-output analysis has suffered from the lack of adequate and detailed data at different subnational levels. The problem becomes particularly acute in less developed regions, where resources to gather information are seldom available. Scholars agree in that hybrid approaches to construct regional input-output models are the most cost-effective alternative. The aim of this thesis is to expand the toolbox that regional input-output modellers have in hand with new hybrid techniques. In this vein, I introduce three methodological alternatives that relax information requirements to solve certain modelling challenges.