nep-net New Economics Papers
on Network Economics
Issue of 2022‒07‒18
ten papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Learning in Canonical Networks By Choi, S.; Goyal, S.; Moisan, F.; To, Y. Y. T.
  2. Are decentralized finance really decentralized? A social network analysis of the Aave protocol on the Ethereum blockchain By Ziqiao Ao; Gergely Horvath; Luyao Zhang
  3. Key players in bullying networks By Ata Atay; Ana Mauleon; Simon Schopohl; Vincent Vannetelbosch
  4. Quantifying impact and response in markets using information filtering networks By Seabrook, Isobel; Caccioli, Fabio; Aste, Tomaso
  5. A Network Perspective in Supply Chain Risk Management By Bier, Tobias
  6. Talent Flow Network, the Life Cycle of Firms, and Their Innovations By Mai, Nhat Chi
  7. Misinformation due to asymmetric information sharing By Buechel, Berno; Klößner, Stefan; Meng, Fanyuan; Nassar, Anis
  8. Social distancing beliefs and human mobility: Evidence from Twitter By Simon Porcher; Thomas Renault
  9. The Slippery Slope from Pluralistic to Plural Societies By Nicola Campigotto; Chiara Rapallini; Aldo Rustichini
  10. Defence partnerships, military expenditure, investment, and economic growth: an analysis in PESCO countries By Dimitrios Karamanis

  1. By: Choi, S.; Goyal, S.; Moisan, F.; To, Y. Y. T.
    Abstract: Subjects observe a private signal and then make an initial guess; they observe their neighbors’ guesses and guess again, and so forth. We study learning dynamics in three networks: Erdös-Rényi, Stochastic Block (reflecting homophily) and Royal Family (that accommodates both highly connected celebrities and local intearctions). We find that the Royal Family network is more likely to sustain incorrect consensus and that the Stochastic Block network is more likely to persist with diverse beliefs. These aggregate patterns are consistent with individuals following DeGroot updating rule.
    Keywords: consensus, experimental social science, social learning, social networks
    JEL: C91 C92 D83 D85
    Date: 2022–06–01
  2. By: Ziqiao Ao; Gergely Horvath; Luyao Zhang
    Abstract: Decentralized finance (DeFi) has the potential to disrupt centralized finance by validating peer-to-peer transactions through tamper-proof smart contracts and thus significantly lower the transaction cost charged by financial intermediaries. However, the actual realization of peer-to-peer transactions and the levels and effect of decentralization are largely unknown. Our research pioneers a blockchain network study that applies social network analysis to measure the level, dynamics, and impacts of decentralization in DeFi token transactions on Ethereum blockchain. First, we find a significant core-periphery structure in the AAVE token transaction network where the cores include the two largest centralized crypto exchanges. Second, we provide evidence that multiple network features consistently characterize decentralization dynamics. Finally, we document that a more decentralized network significantly predicts a higher return and lower volatilities of the DeFi tokens. We point out that our approach is seminal for inspiring future extensions related to the facets of application scenarios, research questions, and methodologies on the mechanics of blockchain decentralization.
    Date: 2022–06
  3. By: Ata Atay (Universitat de Barcelona and BEAT); Ana Mauleon (CEREC, UCLouvain Saint-Louis Brussels and CORE/LIDAM, UCLouvain, Belgium); Simon Schopohl (CEREC, UCLouvain Saint-Louis Brussels, Belgium); Vincent Vannetelbosch (CORE/LIDAM, UCLouvain, Belgium)
    Abstract: Individuals are embedded in a network of relationships and they can be victims, bystanders, or perpetrators of bullying and harassment. Each individual decides noncooperatively how much effort to exert in preventing misbehavior. Each individual's optimal effort depends on the contextual effect, the social multiplier effect and the social conformity effect. We characterize the Nash equilibrium and we derive an inter-centrality measure for finding the key player who once isolated increases the most the aggregate effort. An individual is more likely to be the key player if she is influencing many other individuals, she is exerting a low effort because of her characteristics, and her neighbors are strongly influenced by her. The key player policy increases substantially the aggregate effort and the targeted player should never be selected randomly. The key player is likely to remain the key player in presence of social workers except if she is becoming much less influential due to her closeness to social workers. Finally, we consider alternative policies (e.g. training bystanders for helping victims) and compare them to the policy of isolating the key player.
    Keywords: Social networks, bullying, harassment, peer effects, key player, conformity,
    JEL: A14 C72 D85 Z13
    Date: 2022
  4. By: Seabrook, Isobel; Caccioli, Fabio; Aste, Tomaso
    Abstract: We present a novel methodology to quantify the 'impact' of and 'response' to market shocks. We apply shocks to a group of stocks in a part of the market, and we quantify the effects in terms of average losses on another part of the market using a sparse probabilistic elliptical model for the multivariate return distribution of the whole market. Sparsity is introduced with an L0-norm regularization, which forces to zero some elements of the inverse covariance according to a dependency structure inferred from an information filtering network. Our study concerns the FTSE 100 and 250 markets and analyzes impact and response to shocks both applied to and received from individual stocks and group of stocks. We observe that the shock pattern is related to the structure of the network associated with the sparse structure of the inverse covariance of stock log-returns. Central sectors appear more likely to be affected by shocks, and stocks with a large level of underlying diversification have a larger impact on the rest of the market when experiencing shocks. By analyzing the system during times of crisis and comparative market calmness, we observe changes in the shock patterns with a convergent behavior in times of crisis.
    Keywords: stress testing; systemic risk; elliptical conditional probability; financial modeling; (ES/K002309/1; (EP/P031730/1); H2020-ICT-2018-2 825215
    JEL: F3 G3
    Date: 2022–05–09
  5. By: Bier, Tobias
    Abstract: Classical approaches in the field of supply chain risk management (SCRM) consider supply chains as linear, as the term itself indicates (Hearnshaw and Wilson, 2013). However, modern supply chains are by no means linear—they form complex interconnected networks (e.g., Hearnshaw and Wilson (2013)). This increased complexity is induced by trends such as globalization, increasing product complexity and shorter lead times (Ghadge et al., 2013, Harland et al., 2003). Clearly, new methods for supply chain management are needed, especially those that consider the complexity of today’s supply chains. In this respect, the network structure of supply chains also needs to be considered. For example, studies find that the supply network structure is directly related to resilience, which is the key to effective SCRM (Kim et al., 2015). Research has introduced network theoretical approaches to supply chain management (e.g., Galaskiewicz (2011), Borgatti and Li (2009)). This cumulative dissertation joins the effort by addressing the research field of network theory in the SCRM context. This dissertation contributes to the domain, by first providing a systematic literature review that structures methods for mitigating disruptions in complex supply chains – or to be precise supply networks – and outlines an agenda for further research in the field. Next, in the second paper, it contributes a qualitative model that helps to understand the mechanisms of risks in complex supply chain networks. The same model is the basis for two quantitative studies conveyed in the third and fourth papers that investigate how centrality measures can be used to identify critical suppliers. Finally, the fifth paper conveys a study which directly contributes to practice by developing a supply chain mapping framework as a basis for systematic, effective, and efficient SCRM in complex supply chain networks.
    Date: 2022
  6. By: Mai, Nhat Chi
    Abstract: This paper explores how talent flow network and the firm life cycle affect the innovative performances of firms. This study first established an interorganizational talent flow network with the occupational mobility data available from the public resumes on LinkedIn China. Thereafter, this information was combined with the financial data of China’s listed companies to develop a unique dataset for the time period between 2000 and 2015. The empirical results indicate the following: (1) the breadth and depth of firms’ embedding in the talent flow network positively impact their innovative performances; (2) younger firms’ innovations are mostly promoted by the breadth of network embedding, but this positive effect weakens as firms increase in age; (3) mature firms’ innovations are primarily driven by the depth of network embedding, and this positive effect strengthens as firms increase in age. This paper enriches and deepens the studies of talent flow networks, and it provides practical implications for innovation management based on talent flow for various types of firms at different development stages.
    Date: 2022–03–27
  7. By: Buechel, Berno; Klößner, Stefan (Universität Vechta); Meng, Fanyuan (University of Fribourg, Switzerland); Nassar, Anis (University of Fribourg, Switzerland)
    Abstract: On social media platforms, true and false information compete. Importantly, some messages travel much further than others, even if they concern the same topic. This fact is not reflected in models of social learning (or opinion formation) in networks. Our model fills this gap by allowing different types of information to have different decay factors and to be shared to different networks of people, incorporating asymmetries in sharing behaviors. More “shareable†information then dominates in the long run. This yields a substantial probability of misinformation, in contrast to the special case of symmetry covered by the literature. Asymptotic learning requires a perfect balance between two types of asymmetry: the product of decay factor and largest eigenvalue in the respective signal sharing networks must coincide. Approaching this balance reduces the speed of convergence and enables social learning in the shorter term. Our analysis thus suggests that policy makers, who do not know the true state, aim to mitigate asymmetries in signal sharing, e.g. by weakening echo chambers or by fostering the shareability of cumbersome, boring messages.
    Keywords: misinformation; asymmetry; social networks; social learning; opinion dynamics; echo chambers
    JEL: D83 D85
    Date: 2022–06–24
  8. By: Simon Porcher (IAE Paris - Sorbonne Business School); Thomas Renault (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We construct a novel database containing hundreds of thousands geotagged messages related to the COVID-19 pandemic sent on Twitter. We create a daily index of social distancing—at the state level—to capture social distancing beliefs by analyzing the number of tweets containing keywords such as "stay home", "stay safe", "wear mask", "wash hands" and "social distancing". We find that an increase in the Twitter index of social distancing on day t-1 is associated with a decrease in mobility on day t. We also find that state orders, an increase in the number of COVID-19 cases, precipitation and temperature contribute to reducing human mobility. Republican states are also less likely to enforce social distancing. Beliefs shared on social networks could both reveal the behavior of individuals and influence the behavior of others. Our findings suggest that policy makers can use geotagged Twitter data—in conjunction with mobility data—to better understand individual voluntary social distancing actions.
    Date: 2021
  9. By: Nicola Campigotto; Chiara Rapallini; Aldo Rustichini
    Abstract: Academic consensus about normative prescriptions on the ethnic and cultural composition of societies has been shifting in recent decades. It has evolved from what seemed desirable but was acknowledged to be unrealistic (the noble idea of a melting pot), to what is realistic because it has already happened, but might be undesirable in the long run: the multicultural diaspora. Plural societies, an unintended consequence of multiculturalism, lurk in the background. Thus scholars of social and economic questions, as well as societies, face a threehorned dilemma. We throw some light on the dilemma by examining school friendship networks in five European countries with recent immigration. Our results highlight the force of elective affinities in overcoming differences, but they also point to the countervailing forces of elective discordance that are currently driving increasing division.
    Keywords: Friendship; Homophily; Immigration; Networks; Social cohesion.
    JEL: D85 J15 Z13
    Date: 2022
  10. By: Dimitrios Karamanis
    Abstract: This paper employs a panel vector autoregressive (PVAR) approach to investigate the relationship among military expenditure, investment, and economic growth, over the period after the enforcement of the Maastricht treaty (1994–2018) in 25 European countries that participate in the Permanent Structured Cooperation (PESCO). By using the Louvain community detection algorithm on the network links that have been established through defence partnerships in PESCO projects, two different country clusters emerge. Findings suggest that military expenditures can stimulate economic growth but the effects may not be common for all Member States, which might benefit from the involvement in joint defence projects to maximize the effectiveness of their defence spending.
    Keywords: Defence; Military; Military Expenditure; Investment; Economic Growth; PESCO
    Date: 2022–07

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