The paper aims to analyse the behaviour of a battery of non-survey techniques of constructing regional I-O tables in estimating impact. For this aim, a Monte Carlo simulation, based on the generation of 'true' multiregional I-O tables, was carried out. By aggregating multi-regional I-O tables, national I-O tables were obtained. From the latter, indirect regional tables were derived through the application of various regionalisation methods and the relevant multipliers were compared with the 'true' multipliers using a set of statistics. Three aspects of the behaviour of the methods have been analysed: performances to reproduce 'true' multipliers, variability of simulation error and direction of bias. The results have demonstrated that the Flegg et al. Location Quotient (FLQ) and its augmented version (AFLQ) represent an effective improvement of conventional techniques based on the use of location quotients in both reproducing 'true' multipliers and generating more stable simulation errors. In addition, the results have confirmed the existence of a tendency of the methods to over/underestimate impact. In the cases of the FLQ and the AFLQ, this tendency depends on the value of the parameter d.

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The objective of this article is twofold: first, investigating the relationship between technical efficiency and decoupled direct payments of a sample of Italian farms prior to the application of the 2014–2020 Common Agricultural Policy reform; second, evaluating possible implications of alternative scenarios about distribution of direct payments on technical efficiency. To these aims, a stochastic frontier analysis is adopted. Results indicate that direct payments produce significant effects on technical efficiency in specialized farms, which received higher levels of support. However, effects are contrasting. Moreover, results show that redistribution of policy subsidies may negatively impact on technical efficiency to an extent depending upon the criterion of redistribution applied. Finally, some policy suggestions are given.

This paper is concerned with two parameterized methods of regionalising input-output coefficients: the Flegg et al. Location Quotient (FLQ) and its augmented version (AFLQ). For applying the two techniques, a parameter δ has to be estimated. In this regard, the paper faces two matters that are still open in the literature: the existence of a range of δ that can be used in different regions and the estimation of the most appropriate value of δ. For this aim, a Monte Carlo simulation has been carried out in order to generate 'true' multiregional I-O tables randomly. From the simulation, analyses based on probability distributions and regression were also carried out. Finally, these simulation results have been compared with those of an empirical case. Results confirm that there is actually a range of values of δ within which the best δ is more likely to fall. For the FLQ, this range is centred on 0.3 with an associated probability of 33% (if the width of the range is set at 0.1), whereas, for the AFLQ, the relevant range is between 0.3 and 0.4 with a probability by 38%. Finally, this paper provided a way to estimate the best δ for a given region, without knowing the relevant and detailed economic structure at sectoral level.Non-survey techniques, Input-output matrices, Monte Carlo simulation,