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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.

This paper investigates the macroeconomic determinants of global bilateral remittances flows. Using data covering 216 World countries over the 2010-2017 period, we employ a gravity-model approach to explore the role payed by dyadic and country-specific covariates in explaining remittances. Robustly across alternative estimation techniques and specifications, we find that remittance flows are 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 in the home country. We also document the existence of a robust non-linear relationship between per-capita income at home and remittance flows, which we study both in the aggregate and in subsamples where home and host countries are categorized according to their income group. Overall, our results suggest that altruistic and self-interested motives non-trivially interact and may change across both host/home income groups and the level of income at home.

The Great Recession seems to be a natural experiment for economic analysis, in that it has shown the inadequacy of the predominant theoretical framework - the New Neoclassical Synthesis (NNS) - grounded on the DSGE model. In this paper, we present a critical discussion of the theoretical, empirical and political-economy pitfalls of the DSGE-based approach to policy analysis. We suggest that a more fruitful research avenue should escape the strong theoretical requirements of NNS models (e.g., equilibrium, rationality, representative agent, etc.) and consider the economy as a complex evolving system, i.e. as an ecology populated by heterogenous agents, whose far-from-equilibrium interactions continuously change the structure of the system. This is indeed the methodological core of agent-based computational economics (ACE), which is presented in this paper. We also discuss how ACE has been applied to policy analysis issues, and we provide a survey of macroeconomic policy applications (fiscal and monetary policy, bank regulation, labor market structural reforms and climate change interventions). Finally, we conclude by discussing the methodological status of ACE, as well as the problems it raises.

The Great Recession seems to be a natural experiment for economic analysis, in that it has shown the inadequacy of the predominant theoretical framework | the New Neoclassical Synthesis (NNS) | grounded on the DSGE model. In this paper, we present a critical discussion of the theoretical, empirical and political-economy pitfalls of the DSGE-based approach to policy analysis. We suggest that a more fruitful research avenue should escape the strong theoretical requirements of NNS models (e.g., equilibrium, rationality, representative agent, etc.) and consider the economy as a complex evolving system, i.e. as an ecology populated by heterogeneous agents, whose far-from-equilibrium interactions continuously change the structure of the system. This is indeed the methodological core of agent-based computational economics (ACE), which is presented in this paper. We also discuss how ACE has been applied to policy analysis issues, and we provide a survey of macroeconomic policy applications ( fiscal and monetary policy, bank regulation, labor market structural reforms and climate change interventions). Finally, we conclude by discussing the methodological status of ACE, as well as the problems it raises.

The Great Recession seems to be a natural experiment for economic analysis, in that it has shown the inadequacy of the predominant theoretical framework | the New Neoclassical Synthesis (NNS) | grounded on the DSGE model. In this paper, we present a critical discussion of the theoretical, empirical and political-economy pitfalls of the DSGE-based approach to policy analysis. We suggest that a more fruitful research avenue should escape the strong theoretical requirements of NNS models (e.g., equilibrium, rationality, representative agent, etc.) and consider the economy as a complex evolving system, i.e. as an ecology populated by heterogeneous agents, whose far-from-equilibrium interactions continuously change the structure of the system. This is indeed the methodological core of agent-based computational economics (ACE), which is presented in this paper. We also discuss how ACE has been applied to policy analysis issues, and we provide a survey of macroeconomic policy applications ( fiscal and monetary policy, bank regulation, labor market structural reforms and climate change interventions). Finally, we conclude by discussing the methodological status of ACE, as well as the problems it raises.

AbstractThis paper asks whether the level of integration of world countries in the international network of temporary human mobility can explain differences in their per-capita income and labor productivity. We disentangle the role played by global country centrality in the network from traditional openness measures, which only account for local, nearest-neighbor linkages through which ideas and knowledge can flow. Using 1995-2010 data, we show that global country centrality in the international temporary human-mobility network enhances both per-capita income and labor productivity. Our results hold cross-sectionally, as well as in a dynamic-panel estimation, and take into account potential endogeneity issues. Our findings imply that how close a country is to the theoretical technological frontier, depends not only on how much she is open to temporary human mobility, but mostly on whether she is embedded in a web of relationships connecting her with other influential partners in the network. Our exercises also suggest that most of the gain in income and productivity can be attained if country centrality in the network comes mostly from influential partners that lie not too far away from, but neither too close to them in the network.