Agent-based models (ABMs) are increasingly used to study complex economic phenomena such as endogenous growth, but their analysis typically relies on ad-hoc Monte Carlo exercises without formal statistical guarantees. We show how statistical model checking (SMC), and in particular Multi-VeStA, can automate and enrich the analysis of a seminal ABM: the Island Model of Fagiolo and Dosi, which captures the exploration-exploitation trade-off in technological search. We reproduce key stylized facts from the original model with formal confidence intervals, confirm the optimality of moderate exploration rates, and perform a counterfactual sensitivity analysis across returns to scale, skill transfer, and knowledge locality. Using MultiVeStA's built-in Welch's t-test, 6 out of 7 pairwise parameter comparisons yield statistically different growth trajectories, while the exception reveals a saturation effect in knowledge locality. Our results demonstrate that SMC offers a principled, reproducible methodology for the quantitative analysis of agent-based economic models.

Agent-based models (ABMs) are increasingly used in macroeconomics, but their analysis still often relies on ad hoc Monte Carlo campaigns with heterogeneous statistical effort across parameter settings. We show how statistical model checking (SMC), implemented through MultiVeStA, can provide a principled analysis layer for a realistic macroeconomic ABM without rewriting the simulator in a dedicated formalism. Our case study is the heuristic-switching Keynes+Schumpeter(K+S) model, analysed hrough a transient sensitivity campaign over one-parameter sweeps, two macro observables (unemployment and GDP growth), and one auxiliary micro-level probe (market share) on the post-warmup phase of a 600-step horizon. The analysis is driven by reusable temporal queries, observable-specific precision targets, and confidence-based stopping rules that automatically determine the simulation effort required by each configuration. Results show a clear contrast across parameter families: macro-financial and structural sweeps produce the strongest transient effects, whereas several heuristic-rule sweeps remain much weaker under the same precision policy. More broadly, the paper shows that SMC can support reproducible and informative quantitative analysis of substantively rich economic ABMs, while making uncertainty estimates and simulation cost explicit parts of the reported results.

Production networks influence economic outcomes through several channels. On the one hand, linkages within such networks enhance productivity and have been pivotal for development. On the other hand, integration into Global Value Chains (GVCs) can drive economic upgrading. This paper provides a comprehensive empirical analysis of these mechanisms over the long run. Using a long-run multi-country input-output dataset (1965-2014), we trace the origins and evolution of global production networks (GPNs). By employing parsimonious measures of production structure (namely, upstreamness and downstreamness), we show that both national and international linkages relate to different dimensions of economic performance. Although highly correlated, the two measures exhibit distinct associations with sectoral performance. At the aggregate country level, only downstreamness remains robustly associated with growth, even after controlling for an extensive set of covariates. Finally, we show that growth is not driven by centrality in GPNs per se, but by embeddedness through its connection to the share of intermediate goods in production.

We evaluate the exposure of Italian regions to employment and the health risk associated with the spread of COVID-19. First, we estimate the degree of participation of Italian regions in a plurality of value chains linked to consumption, investment and exports. Second, we investigate the different levels of contagion risk associated with each value chain and the possibility of reducing such risk through remote work. We find that regions are affected differently by lockdown policies because of their highly heterogeneous embeddedness in different value chains, and their diverse sectoral contributions to each of them.

High-resolution gridded climate data are readily available from multiple sources, yet climate research and decision-making increasingly require country and region-specific climate information weighted by socio-economic factors. Moreover, the current landscape of disparate data sources and inconsistent weighting methodologies exacerbates the reproducibility crisis and undermines scientific integrity. To address these issues, we have developed a globally comprehensive dataset at both country (GADM0) and region (GADM1) levels, encompassing various climate indicators (precipitation, temperature, SPEI, wind gust). Our methodology involves weighting gridded climate data by population density, night-time light intensity, cropland area, and concurrent population count – all proxies for socio-economic activity – before aggregation. We process data from multiple sources, offering daily, monthly, and annual climate variables spanning from 1900 to 2023. A unified framework streamlines our preprocessing steps, and rigorous validation against leading climate impact studies ensures data reliability. The resulting Weighted Climate Dataset is publicly accessible through an online dashboard at https://weightedclimatedata.streamlit.app/.

The idiosyncratic (microscopic) and systemic (macroscopic) components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic ‘equally-weighted’ portfolio. In this paper, we exploit clustering techniques derived from Random Matrix Theory (RMT) to study a third, intermediate (mesoscopic) market structure that turns out to be the most stable over time and provides important practical insights from a portfolio management perspective. First, we illustrate the benefits, in terms of predicted and realized risk profiles, of constructing portfolios by filtering out both random and systemic comovements from the correlation matrix. Second, we redefine the portfolio optimization problem in terms of stock clusters that emerge after filtering. Finally, we propose a new wealth allocation scheme that attaches equal importance to stocks belonging to the same community and show that it further increases the reliability of the constructed portfolios. Results are robust across different time spans, cross-sectional dimensions and set of constraints defining the optimization problem

Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (total precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density, night-time light intensity, cropland, and concurrent population count - all proxies of economic activity - before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, and we validate our data through a systematic comparison with those employed in leading climate impact studies.

This paper investigates the macroeconomic determinants of global bilateral remittance flows. Unlike existing studies, which have been often hampered by the lack of comprehensive and large-enough datasets, we use data originally covering 214 countries over the 2010–2017 period. We employ a gravity-model approach to explore the role played by dyadic and country-specific covariates in explaining remittances. We find that remittance flows are robustly and strongly impacted by size effects (i.e., number of migrants in the host country and population at home), transaction costs, common social, political, and cultural ties, output growth rate, and financial development at home. We also document the existence of a robust non-linear relationship between per capita income at home and remittance flows, both in the aggregate and across income groups.

This work addresses the role of inter-sectoral innovation flows, which we frame as technological interdependencies, in determining sectoral employment dynamics. This purpose is achieved through the construction of an indicator capturing the amount of R&D expenditures embodied in the backward linkages of industries. We aim to find out whether having a more integrated production in terms of requiring more technological inputs is related to a lower demand for workers within the sector. We refer to the literature on innovation-employment nexus, inter-sectoral knowledge spillovers and Global Value Chains, building upon structuralist and evolutionary theoretical considerations. We track the flows of embodied technological change between industries taking advantage of the notion of vertically integrated sectors. The relevance of this vertical technological dimension for determining employment dynamics is then tested on a panel data of European industries over the 2008-2014 period. Results show a statistically significant and negative employment impact of the degree of vertical integration in terms of acquisitions of R&D embodied inputs. Combining the role of demand, the double nature of innovation - as product and as process -, together with inter-sectoral linkages, this work shows that the dependence of a sector from innovation performed by other ones - a proxy for input embodied process innovations - exert a negative effect upon employment.

Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (average precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density or by night light intensity -- both proxies of economic activity -- before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, which we share in the open-access Weighted Climate Data Repository web app. Finally, we validate our data through a systematic comparison with those employed in leading climate impact studies.

This paper empirically investigates the role played by cross-country spillovers in shaping spatiotemporal differences in country income. While existing literature focused on effects captured by direct spillovers with partner countries only, here we take a complex network perspective to explore whether the global embeddedness of countries in the macroeconomic multi-network may significantly impact income, net of country local characteristics such as local foreign exposure. We employ data for the period 2000–2020 to build a time sequence of 3-layer multi graphs, with countries as nodes and links weighted by the intensity of bilateral relations in international trade, finance and human migration. Using panel-regression techniques, we then ask if country (eigenvector) centrality in the multi network can account for parts of the observed heterogeneity in country per-capita income, both cross-sectionally and over time. Robustly across a number of alternative specifications of the empirical model, we find that being more central significantly boosts country income. This implies that income-enhancing technological spillovers are not only channeled via local exposure, but also through indirect interactions with more distant nodes.

We build a novel computational input-output model to estimate the economic impact of lockdowns in Italy. The key advantage of our framework is to integrate the regional and sectoral dimensions of economic production in a very parsimonious numerical simulation framework. Lockdowns are treated as shocks to available labor supply and they are calibrated on regional and sectoral employment data coupled with the prescriptions of government decrees. We show that when estimated on data from the first “hard” lockdown, our model closely reproduces the observed economic dynamics during spring 2020. In addition, we show that the model delivers a good out-of-sample forecasting performance. We also analyze the effects of the second “mild” lockdown in fall of 2020 which delivered a much more moderate negative impact on production compared to both the spring 2020 lockdown and to a hypothetical second “hard” lockdown.

In this paper we characterize the performance of venture capital-backed firms based on their ability to attract investment. The aim of the study is to identify relevant predictors of success built from the network structure of firms’ and investors’ relations. Focusing on deal-level data for the health sector, we first create a bipartite network among firms and investors, and then apply functional data analysis to derive progressively more refined indicators of success captured by a binary, a scalar and a functional outcome. More specifically, we use different network centrality measures to capture the role of early investments for the success of the firm. Our results, which are robust to different specifications, suggest that success has a strong positive association with centrality measures of the firm and of its large investors, and a weaker but still detectable association with centrality measures of small investors and features describing firms as knowledge bridges. Finally, based on our analyses, success is not associated with firms’ and investors’ spreading power (harmonic centrality), nor with the tightness of investors’ community (clustering coefficient) and spreading ability (VoteRank).

In this article, the author studies epidemic diffusion in a spatial compartmental model, where individuals are initially connected in a social or geographical network. As the virus spreads in the network, the structure of interactions between people may endogenously change over time, due to quarantining measures and/or spatial-distancing (SD) policies. The author explores via simulations the dynamic properties of the coevolutionary process linking disease diffusion and network properties. Results suggest that, in order to predict how epidemic phenomena evolve in networked populations, it is not enough to focus on the properties of initial interaction structures. Indeed, the coevolution of network structures and compartment shares strongly shape the process of epidemic diffusion, especially in terms of its speed. Furthermore, the author shows that the timing and features of SD policies may dramatically influence their effectiveness.

The unprecedented lockdown measures implemented by many countries in the wake of the COVID-19 pandemic have created a need for tools to assess their economic costs. For this purpose, we present a novel dynamic input-output modelling framework which we apply to an estimation of the economic impact of lockdowns in Italy. Lockdown measures are treated as shocks to available labor supply, being calibrated on regional and sectoral employment data coupled with the prescriptions of the prime ministerial decrees mandating the closure of specific industries. Using input-output tables for the Italian regions, we estimate the model on data from the first lockdown during spring 2020 and then simulate it to assess the regional and sectoral impacts. We find that, despite the simplicity of our framework, the model is able to reproduce the observed dynamics during the lockdown-induced downturn and subsequent recovery fairly closely for most sectors. This ability to match the empirical data is also confirmed by a small out-of-sample forecasting exercise. We subsequently also simulate the second set of ‘softer’ lockdown measures implemented during autumn and winter of 2020 in order to evaluate their impact and compare them to the first, ‘hard’ lockdown. Overall, we believe the simplicity and parsimony of our framework make it suitable for providing quick and reasonably accurate evaluations of the economic effects of different lockdown measures.

Understanding specialization patterns of countries in food production can provide relevant insights for the evaluation and design of policies seeking to achieve food security and sustainability, which are key to reach several Sustainable Development Goals (SDGs). This paper builds bipartite networks of food products and food-producing countries, using FAO data from 1993 to 2013, to characterize the global food production system. We use methods from complex systems analysis to rank products according to their need for capabilities and countries according to their competitiveness, which derives from the quality and diversification of their food production baskets. We observe two well-defined communities of food-producing countries, one that groups countries with relatively developed agricultural systems, and the other grouping countries with less developed production systems. The stability of these two communities reveals persistent differences between countries specialization patterns. We econometrically analyze whether and how specialization patterns affect food supply, food security (SDGs: Targets 2.1 and 2.2), and sustainability of food systems (SDGs: Target 2.4). We show that concentrating agricultural production negatively impacts food supply, food security, and food systems sustainability. The competitiveness of countries and the coherence of their diversification patterns increase per capita food supply and food security but might harm sustainability. This evidence reflects the trade-off between achieving food security while simultaneously improving sustainability, which needs to be considered when developing or implementing policies seeking to reach SDGs. (C) 2021 Elsevier Ltd. All rights reserved.

This paper extends the endogenous-growth agent-based model in Fagiolo and Dosi (2003) to study the financegrowth nexus. We explore industries where firms produce a homogeneous good using existing technologies, perform R&D activities to introduce new techniques, and imitate the most productive practices. Unlike the original model, we assume that both exploration and imitation require resources provided by banks, which pool agent savings and finance new projects via loans. We find that banking activity has a positive impact on growth. However, excessive financialization can hamper growth. In- deed, we find a significant and robust inverted-U shaped relation between financial depth and growth. Overall, our results stress the fundamental (and still poorly understood) role played by innovation in the finance-growth nexu

In the last years, there has been a growing interest in studying the global food system as a complex evolving network. Much of the literature has been focusing on the way countries are interconnected in the food system through international-trade linkages, and what consequences this may have in terms of food security and sustainability. Little attention has been instead devoted to understanding how countries, given their capabilities, specialize in agricultural production and to the determinants of country specialization patterns. In this paper, we start addressing this issue using FAO production data for the period 1993-2013. We characterize the food production space as a time-sequence of bipartite networks, connecting countries to the agricultural products they produce, and we identify properties and determinants underlying their evolution. We find that the agricultural product space is a very dense network, which however displays well-defined and stable communities of countries and products, despite the unprecedented pressure that food systems have been undergoing in recent years. We also find that the observed community structures are not only shaped by agro-ecological conditions but also by economic, socio-political, and technological factors. Finally, we discuss the implications that such findings may have on our understanding of the complex relationships involving country production capabilities, their specialization patterns, food security, and the nutrition content of the domestic part of their food supply.

In this paper, I study epidemic diffusion in a generalized spatial SEIRD model, where individuals are initially connected in a social or geographical network. As the virus spreads in the network, the structure of interactions between people may endogenously change over time, due to quarantining measures and/or spatial-distancing policies. I explore via simulations the dynamic properties of the co-evolutionary process dynamically linking disease diffusion and network properties. Results suggest that, in order to predict how epidemic phenomena evolve in networked populations, it is not enough to focus on the properties of initial interaction structures. Indeed, the co-evolution of network structures and compartment shares strongly shape the process of epidemic diffusion, especially in terms of its speed. Furthermore, I show that the timing and features of spatial-distancing policies may dramatically influence their effectiveness.

In this work we introduce and analyze a new and comprehensive multilayer dataset covering a wide spectrum of international relationships between coutries. We select two cross sections of the dataset corresponding to years 2003 and 2010 with 19 layers and 112 nodes to study the structure and evolution of the network. Country centrality is measured by the multiplex PageRank (MultiRank) and the multiplex hub and authority scores (MultiHub and MultiAuth). We find that the MultiHub measure has the highest correlation to GDP per capita, with respect to the other multilayer measures and to their single layer analogues. Finally we analyze the differences in the ranking between GDP per capita and the multilayer centrality measures to evaluate them as measures of development.