意大利银行

We leverage a quasi-experimental research design to estimate the economic effects of the two major anti-mafia policies in Italy, namely firm seizures and city council dismissals. Seizures imply firm exits that increase revenues and profit margins of surviving firms, mainly in municipalities with lower firm density and profitability, and among firms producing differentiated products. Dismissals imply demand contractions that reduce revenues, while profit margins are only marginally affected among firms competing locally. Province-level analysis accounting for spatial spillovers supports results at firm level. In both cases, the analysis suggests that the anti-mafia enforcement intervention reshapes market conditions through separate, yet related, mechanisms and that the degree of labour-cost adjustment determines the extent to which changes in revenues translate into profits.

Tax‒benefit microsimulation models are typically used to quantify the effect of specific policy changes on the income distribution based on representative microdata. Such analysis evaluates policies by considering how different tax‒benefit elements interact given personal, household and labour market characteristics. Using hypothetical household data instead helps address broader questions of policy design and systemic (cross-national) differences. This article introduces the Hypothetical Household Tool (HHoT) in combination with the microsimulation model EUROMOD to analyse European tax‒benefit policies from a comparative perspective. It presents a series of applications from social welfare analysis illustrating how hypothetical data can benefit comparative academic and policy research.

Current macroeconomic models with agent heterogeneity can be broadly divided into two main groups. Heterogeneous-agent general equilibrium (GE) models, such as those based on Heterogeneous Agent New Keynesian (HANK) or Krusell-Smith (KS) approaches, rely on GE and 'rational expectations', somewhat unrealistic assumptions that make the models very computationally cumbersome, which in turn limits the amount of heterogeneity that can be modelled. In contrast, agent-based models (ABMs) can flexibly encompass a large number of arbitrarily heterogeneous agents, but typically require the specification of explicit behavioural rules, which can lead to a lengthy trial-and-error model-development process. To address these limitations, we introduce MARL-BC, a framework that integrates deep multi-agent reinforcement learning (MARL) with real business cycle (RBC) models. We demonstrate that MARL-BC can: (1) recover textbook RBC results when using a single agent; (2) recover the results of the mean-field KS model using a large number of identical agents; and (3) effectively simulate rich heterogeneity among agents, a hard task for traditional GE approaches. Our framework can be thought of as an ABM if used with a variety of heterogeneous interacting agents, and can reproduce GE results in limit cases. As such, it is a step towards a synthesis of these often opposed modelling paradigms.

This paper studies the short-run economic impact of unanticipated disruptions to inputs potentially critical for domestic production and the green transition, and prone to geopolitical weaponisation (Foreign Critical Inputs - FCIs). Using firm-level customs and balance-sheet data for Belgium, France, Italy, Slovenia, and Spain, we find that a 50% reduction in FCI imports from China-aligned countries could lead to an average decline in manufacturing value added of 2.7% across the five countries. The impact, however, is highly uneven across firms, sectors, and regions. These findings highlight that supply disruptions in FCIs can impose substantial short-run economic costs.

The Intrinsic Dimension (ID) is a key concept in unsupervised learning and feature selection, as it is a lower bound to the number of variables which are necessary to describe a system. However, in almost any real-world dataset the ID depends on the scale at which the data are analysed. Quite typically at a small scale, the ID is very large, as the data are affected by measurement errors. At large scale, the ID can also appear erroneously large, due to the curvature and the topology of the manifold containing the data. In this work, we introduce an automatic protocol to select the sweet spot, namely the correct range of scales in which the ID is meaningful and useful. This protocol is based on imposing that for distances smaller than the correct scale the density of the data is constant. In the presented framework, to estimate the density it is necessary to know the ID, therefore, this condition is imposed self-consistently. We illustrate the usefulness and robustness of this procedure to noise by benchmarks on artificial and real-world datasets.