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on Network Economics |
| By: | Leonardo Boncinelli; Alessio Muscillo; Paolo Pin |
| Abstract: | Motivated by data on coauthorships in scientific publications, we analyze a team formation process that generalizes matching models and network formation models, allowing for overlapping teams of heterogeneous size. We apply different notions of stability: myopic team-wise stability, which extends to our setup the concept of pair-wise stability, coalitional stability, where agents are perfectly rational and able to coordinate, and stochastic stability, where agents are myopic and errors occur with vanishing probability. We find that, in many cases, coalitional stability in no way refines myopic team-wise stability, while stochastically stable states are feasible states that maximize the overall number of activities performed by teams. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.13712&r=all |
| By: | Alberto Bisin; Andrea Moro |
| Abstract: | We introduce a model of the diffusion of an epidemic with demographically heterogeneous agents interacting socially on a spatially structured network. Contagion-risk averse agents respond behaviorally to the diffusion of the infections by limiting their social interactions. Firms also respond by allowing employees to work remotely, depending on their productivity. The spatial structure induces local herd immunities along socio-demographic dimensions, which significantly affect the dynamics of infections. We study several non-pharmaceutical interventions; e.g., i) lockdown rules, which set thresholds on the spread of the infection for the closing and reopening of economic activities; and ii) selective lockdowns, which restrict social interactions by location (in the network) and by the demographic characteristics of the agents. Substantiating a "Lucas critique" argument, we assess the cost of naive discretionary policies ignoring agents and firms' behavioral responses. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.13789&r=all |
| By: | V. A. Kalyagin; A. P. Koldanov; P. A. Koldanov |
| Abstract: | Maximum spanning tree (MST) is a popular tool in market network analysis. Large number of publications are devoted to the MST calculation and it's interpretation for particular stock markets. However, much less attention is payed in the literature to the analysis of uncertainty of obtained results. In the present paper we suggest a general framework to measure uncertainty of MST identification. We study uncertainty in the framework of the concept of random variable network (RVN). We consider different correlation based networks in the large class of elliptical distributions. We show that true MST is the same in three networks: Pearson correlation network, Fechner correlation network, and Kendall correlation network. We argue that among different measures of uncertainty the FDR (False Discovery Rate) is the most appropriated for MST identification. We investigate FDR of Kruskal algorithm for MST identification and show that reliability of MST identification is different in these three networks. In particular, for Pearson correlation network the FDR essentially depends on distribution of stock returns. We prove that for market network with Fechner correlation the FDR is non sensitive to the assumption on stock's return distribution. Some interesting phenomena are discovered for Kendall correlation network. Our experiments show that FDR of Kruskal algorithm for MST identification in Kendall correlation network weakly depend on distribution and at the same time the value of FDR is almost the best in comparison with MST identification in other networks. These facts are important in practical applications. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.14593&r=all |
| By: | Benoît Desmarchelier (Université de Lille Faculté des Sciences économiques et sociales); Faridah Djellal (Université de Lille Faculté des Sciences économiques et sociales); Faïz Gallouj (Université de Lille Faculté des Sciences économiques et sociales) |
| Abstract: | This article is dedicated to a consideration of the tertiarization of innovation networks. While the concept of traditional innovation network has been the object of an extensive literature, new expressions of the innovation network appear in a service economy: in particular Public Private Innovation Networks in Services, Market Service Innovation Networks, Public Service Innovation Networks and Public Service Innovation Networks for Social Innovation. They reflect the rise of market and non-market services and of the public-private relationship in collaborative innovation. Based on a literature survey, this article investigates these different expressions of innovation networks and sheds light on the different roles played by public services in each of them. |
| Keywords: | public services,market services,innovation,networks |
| Date: | 2019–11–04 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-03177975&r=all |
| By: | Carolina Mattsson; Frank W. Takes; Eelke M. Heemskerk; Cees Diks; Gert Buiten; Albert Faber; Peter M. A. Sloot |
| Abstract: | Production networks are integral to economic dynamics, yet dis-aggregated network data on inter-firm trade is rarely collected and often proprietary. Here we situate company-level production networks among networks from other domains according to their local connectivity structure. Through this lens, we study a regional and a national network of inferred trade relationships reconstructed from Dutch national economic statistics and re-interpret prior empirical findings. We find that company-level production networks have so-called functional structure, as previously identified in protein-protein interaction (PPI) networks. Functional networks are distinctive in their over-representation of closed squares, which we quantify using an existing measure called spectral bipartivity. Shared local connectivity structure lets us ferry insights between domains. PPI networks are shaped by complementarity, rather than homophily, and we use multi-layer directed configuration models to show that this principle explains the emergence of functional structure in production networks. Companies are especially similar to their close competitors, not to their trading partners. Our findings have practical implications for the analysis of production networks and a thorough understanding of their local connectivity structure will help us better reason about the micro-economic mechanisms behind their routine function, failure, and growth. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.15777&r=all |
| By: | Hamed Amini; Zhongyuan Cao; Agnes Sulem |
| Abstract: | We consider a general tractable model for default contagion and systemic risk in a heterogeneous financial network, subject to an exogenous macroeconomic shock. We show that, under some regularity assumptions, the default cascade model could be transferred to a death process problem represented by balls-and-bins model. We also reduce the dimension of the problem by classifying banks according to different types, in an appropriate type space. These types may be calibrated to real-world data by using machine learning techniques. We then state various limit theorems regarding the final size of default cascade over different types. In particular, under suitable assumptions on the degree and threshold distributions, we show that the final size of default cascade has asymptotically Gaussian fluctuations. We next state limit theorems for different system-wide wealth aggregation functions and show how the systemic risk measure, in a given stress test scenario, could be related to the structure and heterogeneity of financial networks. We finally show how these results could be used by a social planner to optimally target interventions during a financial crisis, with a budget constraint and under partial information of the financial network. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.00248&r=all |
| By: | Mogens Fosgerau; Mads Paulsen; Thomas Kj{\ae}r Rasmussen |
| Abstract: | We propose a model in which a utility maximizing traveler assigns flow across an entire network under a flow conservation constraint. Substitution between routes depends on how much they overlap. This model can be estimated from route choice data, where the full set of route alternatives is included and no choice set generation is required. Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization and is straightforward even for large networks. |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2103.13784&r=all |