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The IEB publishes a new update of its Autonomous Financing Maps

Although Madrid, Catalonia and the Balearic Islands were the communities with the highest fiscal capacity per person in Spain in 2020, in the ranking for the distribution of resources they occupied only the eighth, ninth and tenth positions. The communities that benefited the most were, in this order, Cantabria, La Rioja, Extremadura, Castilla y León, Asturias, Aragón and Galicia. This trend has been repeated, broadly speaking, since the beginning of the application of the current model, in 2009.

These findings are reflected in the seventh update of the Autonomous Financing Maps, published by the Barcelona Institute of Economics (IEB). The maps show the evolution of the resources available in each region per inhabitant with the application of the model’s levelling mechanisms and the adjustment funds.

The IEB’s Autonomous Financing Maps show that some of the communities with the highest fiscal capacity per inhabitant end up receiving a volume of resources that is below the average. In the same way, it is clear that the levelling mechanism, known as the Fund for Guaranteeing Basic Public Services (FGSPF), fulfils the objective of reducing the distance between the communities with different fiscal capacity without altering the initial order. However, the application of the adjustment funds for sufficiency, competitiveness and cooperation generates distortions in distribution. In these circumstances, according to the IEB researchers, a review of the model is justified.

The IEB’s Autonomous Financing Maps offer an updated evaluation and analysis of the current regional financing model, in operation since 2009. Through the different sections of the website, the user can consult the amount of taxes transferred by the central government to each autonomous community, the resources deriving from their participation in the levelling mechanism (the FGSPF) and those deriving from the model’s adjustment funds (the sufficiency, competitiveness and cooperation funds).

The maps depict the four situations that occur before and after the application of the model: 1) communities with below average tax revenue per inhabitant which then receive above average tax revenue; 2) communities that have below average tax revenue per inhabitant and continue in the same situation; 3) communities with above average tax revenue per inhabitant and retain this level; and 4) communities with above average tax revenue per inhabitant but then fall below the average.

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