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on Network Economics |
| By: | Franch, Fabio; Nocciola, Luca; Vouldis, Angelos |
| Abstract: | This paper studies the dynamics of contagion across the banking, insurance and shadow banking sectors of 16 advanced economies in the period 2006-2018. We construct Granger causality-in-risk networks and introduce higher-order aggregate networks and temporal node centralities in an economic setting to capture non-Markovian network features. Our approach uncovers the dynamics of financial contagion as it is transmitted across segments of the financial system and jurisdictions. Temporal centralities identify countries in distress as the nodes through which contagion propagates. Moreover, the banking system emerge as the primary source and transmitter of stress while banks and shadow banks are highly interconnected. The insurance sector is found to contribute less to stress transmission in all periods, except during the global financial crisis. Our approach, as opposed to one that uses memoryless measures of network centrality, is able to identify more clearly the nodes that are critical for the transmission of financial contagion. JEL Classification: C02, C22, G01, G2 |
| Keywords: | financial networks, GARCH, Granger causality-in-tail, non-Markovian, systemic risk |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20222667&r= |
| By: | Christian Cox; Akanksha Negi; Digvijay Negi |
| Abstract: | In a world with costly transfers, some agents with high transaction costs may not find it feasible to trade. Hence, their consumption will co-vary with their endowment, leading to imperfect risksharing. In this paper, we augment the canonical risk-sharing model to incorporate frictions in the form of transaction costs. In this augmented model, given a particular network structure, risk sharing will happen within networks and not the whole universe of agents. We show that transaction costs and the implied network structure of trade have important implications for the tests of risk sharing. Using this model, we derive a structural risk-sharing test that uses consumption and production data alongside the trade network structure. We implement our method using data from the global trade of three major staple food commodities. Comparing our estimates with the benchmark of frictionless trade, we find some evidence of transaction costs impeding risk-sharing in these commodities. |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2022-5&r= |
| By: | Kiran Tomlinson; Austin R. Benson |
| Abstract: | Choices made by individuals have widespread impacts--for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase--moreover, data on these choices are increasingly abundant. Discrete choice models are a key tool for learning individual preferences from such data. Additionally, social factors like conformity and contagion influence individual choice. Existing methods for incorporating these factors into choice models do not account for the entire social network and require hand-crafted features. To overcome these limitations, we use graph learning to study choice in networked contexts. We identify three ways in which graph learning techniques can be used for discrete choice: learning chooser representations, regularizing choice model parameters, and directly constructing predictions from a network. We design methods in each category and test them on real-world choice datasets, including county-level 2016 US election results and Android app installation and usage data. We show that incorporating social network structure can improve the predictions of the standard econometric choice model, the multinomial logit. We provide evidence that app installations are influenced by social context, but we find no such effect on app usage among the same participants, which instead is habit-driven. In the election data, we highlight the additional insights a discrete choice framework provides over classification or regression, the typical approaches. On synthetic data, we demonstrate the sample complexity benefit of using social information in choice models. |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2205.11365&r= |
| By: | Didier Sornette (ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Swiss Finance Institute; Southern University of Science and Technology; Tokyo Institute of Technology); Sandro Claudio Lera (MIT Connection Science); Jianhong Lin (ETH Zurich); Ke Wu (ETH Zurich - Department of Management, Technology, and Economics (D-MTEC); Southern University of Science and Technology) |
| Abstract: | We present a generic new mechanism for the emergence of collective exuberance among interacting agents in a general class of Ising-like models that have a long history in social sciences and economics. The mechanism relies on the recognition that socioeconomic networks are intrinsically non-symmetric and hierarchically organized, which is represented as a non-normal adjacency matrix. Such non-normal networks lead to transient explosive growth (a “bubble”) in a generic domain of control parameters, in particular in the subcritical regime. Contrary to previous models, here the coordination of opinions and actions and the associated global macroscopic order do not require the fine-tuning close to a critical point. This is illustrated in the context of financial markets theoretically, numerically via agent-based simulations and empirically through the analysis of so-called meme stocks. It is shown that the size of the bubble is directly controlled through the Kreiss constant which measures the degree of non-normality in the network. This mapping improves conceptually and operationally on existing methods aimed at anticipating critical phase transitions, which do not take into consideration the ubiquitous non-normality of complex system dynamics. Our mechanism thus provides a general alternative to the previous understanding of instabilities in a large class of complex systems, ranging from ecological systems to social opinion dynamics and financial markets. |
| Keywords: | financial bubbles, non-normal matrices, social networks, sub-criticality, hierarchical networks, anticipating tipping points |
| JEL: | C02 C46 G01 G17 |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2243&r= |
| By: | Alexander M. Petersen |
| Abstract: | We develop a systematic approach to measuring combinatorial innovation in the biomedical sciences based upon the comprehensive ontology of Medical Subject Headings (MeSH). This approach leverages an expert-defined knowledge ontology that features both breadth (27,875 MeSH analyzed across 25 million articles indexed by PubMed from 1902 onwards) and depth (we differentiate between Major and Minor MeSH terms to identify differences in the knowledge network representation constructed from primary research topics only). With this level of uniform resolution we differentiate between three different modes of innovation contributing to the combinatorial knowledge network: (i) conceptual innovation associated with the emergence of new concepts and entities (measured as the entry of new MeSH); and (ii) recombinant innovation, associated with the emergence of new combinations, which itself consists of two types: peripheral (i.e., combinations involving new knowledge) and core (combinations comprised of pre-existing knowledge only). Another relevant question we seek to address is whether examining triplet and quartet combinations, in addition to the more traditional dyadic or pairwise combinations, provide evidence of any new phenomena associated with higher-order combinations. Analysis of the size, growth, and coverage of combinatorial innovation yield results that are largely independent of the combination order, thereby suggesting that the common dyadic approach is sufficient to capture essential phenomena. Our main results are twofold: (a) despite the persistent addition of new MeSH terms, the network is densifying over time meaning that scholars are increasingly exploring and realizing the vast space of all knowledge combinations; and (b) conceptual innovation is increasingly concentrated within single research articles, a harbinger of the recent paradigm shift towards convergence science. |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2205.11632&r= |
| By: | Zoz, Konrad; Zuloaga, Gonzalo; Kulenkampff, Gabriele; Plückebaum, Thomas; Ockenfels, Martin |
| Abstract: | In this study, we analyse regional cost differences of fibre-based access networks. Our data base comprises a complete sample of Very High Capacity Network (VHCN) investment figures. By matching this data with the internationally standardised EUROSTAT and BBSR urban/rural typology classification, we show that such classification criteria do not sufficiently account for a large share of geographical differences in fibre-based access network costs. In order to better explain and/or identify regional differences in VHCN investment, we turn to spatial regression models to identify alternative influencing factors solely on the basis of publicly available data. We show that a handful of geographical factors are capable of explaining 95% of the differences in fibre investment requirements; the most relevant being (1) the size of demand (as number of access lines), (2) the street-based household density (defined as the number of households per kilometre of road in built-up areas), (3) a dispersion measure (approximated by the main road length per built-up area) and (4) the degree of urbanisation (measured by the share of built-up area in relation to the overall area). These results are consistent at different levels of spatial aggregation (e.g. from access areas to NUTS-3 level) and even after controlling for neighbouring effects. Thus, it is capable of predicting costs more precisely and at the level of the territorial unit, at which funds are bounded to be allocated. From a public policy perspective, the proper identification of areas, where the commercial roll-out is unlikely to occur, is key in preventing the widening of a digital gap without having a wasteful use of public funds. |
| Keywords: | Very high capacity networks (VHCN),bottom-up cost models,statistical estimations,spatial analysis,NUTS-3,state aid |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:wikwps:4&r= |
| By: | Ratul Das Chaudhury; C. Matthew Leister; Birendra Rai |
| Abstract: | When can an interest group exploit ideological and affective polarization between political parties to its advantage? We study a model where an interest group credibly promises payments to legislators conditional on voting for its favored policy. Legislators value voting as their friends within their party, and suffer an ideological-disutility upon voting against their party's ideologically preferred policy. Affective polarization, owing to its interpersonal nature, is modeled by assuming a legislator values distinguishing her voting decision from legislators in the opposite party. Our main finding is that an aggregate measure of relative cohesiveness of social networks in the two parties determines whether the interest group can profitably exploit increasing polarization. However, the significance of relative cohesiveness vanishes if there is no ideological polarization between the two parties. |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2205.07486&r= |
| By: | Leogrande, Angelo; Costantiello, Alberto; Laureti, Lucio; Matarrese, Marco Maria |
| Abstract: | The determinants of “International Scientific Co-Publications” in Europe are analyzed in the following article. Data from the European Innovation Scoreboard-EIS of the European Union for 36 countries in the period 2010-2019 were used. The data were analyzed using Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, Dynamic Panel. The results show that the variable “International Scientific Co-Publication” is negatively associated with “Employment Share Manufacturing”, “Intellectual Assets”, “Turnover Share Large Enterprises”, “Linkages” and positively associated with “SMEs innovating in house”, “Trademark Applications”, “Human Resources”, “Publicprivate co-publications”, “Attractive Research Systems”, “Government procurement of advanced technology products”, “Turnover Share SMEs”. Then, a clustering analysis is realized with the algorithm k-Means. The Silhouette Coefficient and the Elbow Method are confronted to optimize the k-Means algorithm. The results show that the Elbow method is more efficient than the Silhouette coefficient in identifying the optimal number of clusters corresponding to k = 4 with the k-Means algorithm. A network analysis was then carried out using the “Manhattan Distance”. The analysis shows the presence of 9 network structures of which 5 are complex i.e., with a number of linkages greater than 3, and 4 are basic i.e. consist of a single link between two countries. Furthermore, we confront eight different machine learning algorithms to predict the future level of “International Scientific Co-Publications”. We found that the best algorithm in performing prediction with original data is the Tree Ensemble Regression. The predicted value of “International Scientific Co-Publication” is expected to growth by 0.61%. |
| Keywords: | Innovation, and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Diffusion Processes; Open Innovation. |
| JEL: | O3 O30 O31 O32 O33 O34 |
| Date: | 2022–05–24 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:113162&r= |
| By: | Hao Wu (Hunan University); Rene van den Brink (Vrije Universiteit Amsterdam); Arantza Estevez-Fernandez (Vrije Universiteit Amsterdam) |
| Abstract: | Cooperative games with a permission structure are useful tools for analyzing the impact of hierarchical structures on allocation problems in Economics and Operations Research. In this paper, we propose a generalization of the local disjunctive and the local conjunctive permission approaches called the k-local permission approach. In this approach, every player needs permission from a certain number of its predecessors to cooperate in a coalition. The special case where every player needs permission from at least one of, respectively all, its predecessors coincides with the local disjunctive, respectively local conjunctive, approach in the literature. We de ne and characterize a corresponding k-local permission value. After that, we apply this value to de ne a new class of power measures for directed graphs. We axiomatize these power measures, and apply some of them to two classical networks in the literature. |
| Keywords: | TU-game, Hierarchical structure, Shapley value, Axiomatization, Digraph, Power measure |
| JEL: | C71 |
| Date: | 2022–06–07 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20220037&r= |