ENBIS Spring Meeting 2019 in Barcelona

13 – 14 June 2019 Abstract submission: 15 January – 20 May 2019

European funds local expenditures simulation: modelling validation and global sensitivity analysis

13 June 2019, 17:45 – 18:10


Submitted by
Samuele Lo Piano
Samuele Lo Piano (Universitat Oberta de Catalunya), Arnald Puy (Université Libre de Bruxelles), Emanuele Borgonovo (Università commerciale Bocconi), Andrea Saltelli (Universitetet i Bergen)
Understanding the spending pattern of European funds is an issue of primary importance as to evaluate their effectiveness in generating benefits at variable spatial and time scales. This especially applies to funds that are remitted at the regional level, such as the ESIF (European Structural and Investment Funds), which represents the main financial tool the European Commission resorts to foster its cohesion policy. However, producing meaningful inference on the local effects yield by these funds faces an inevitable issue of time lagging: The payments from the European Commission are remitted only after the incurred expenses have been invoiced. Precisely, the only official figures available report on the dates when the EU payment were remitted to the local authorities. These figures may be delayed from the actual year on which expenditures were invoiced by the final beneficiaries to the local authorities. These figures are typically not available. Therefore, producing econometric inference on the benefits these ESIF locally produce on the ground may be cumbersome due to this very time lag.

In this contribution, we present a model we developed to simulate the local expenditures on the basis of the observed reimbursement pattern from the European Commission to the European regions. We introduced a new measurement based on the distance of yearly cumulative figures – an index of regional specificity. This parameter along others sampled in plausible uncertainty ranges are used in Monte Carlo simulations to produce expenditure distributions.

The availability of the actual expenditures invoiced to a selection of member state authorities allowed to validate our model through multi-scale benchmarking at different levels of granularity. This comparison resulted in improving the model by tuning the modelling parameters as to reduce the mismatch between the simulated and the actual incurred expenditures. Finally, global sensitivity analysis allowed to apportion the uncertainty in the modelled expenditure against the uncertainty in the input parameters.

This study shows the potential of the combined use of machine-learning type of inference and global sensitivity analysis. The attained advantage is two-fold as it allows to: i) produce a robust modelling activity; ii) flag up the areas mostly affecting the output stochastic uncertainty.

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