nep-net New Economics Papers
on Network Economics
Issue of 2023‒03‒27
seven papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Ruin Probabilities for Risk Processes in Stochastic Networks By Hamed Amini; Zhongyuan Cao; Andreea Minca; Agn\`es Sulem
  2. Online social integration of migrants: evidence from Twitter By Ji Su Kim; Soazic Elise Wang Sonne; Kiran Garimella; André Grow; Ingmar G. Weber; Emilio Zagheni
  3. A Simulation Framework Dedicated to Characterizing Risks and Cascading Effects in Collaborative Networks By Tianyuan Zhang; Jiayao Li; Frederick Benaben
  4. Does the Closeness of Peers Matter? An Investigation Using Online Training Platform Data and Survey Data By Gu, Xin; Li, Haizheng
  5. Spatial Production Networks By Costas Arkolakis; Federico Huneeus; Yuhei Miyauchi
  6. Using firm-level production networks to identify decarbonization strategies that minimize social stress By Johannes Stangl; Andr\'as Borsos; Christian Diem; Tobias Reisch; Stefan Thurner
  7. Estimating the loss of economic predictability from aggregating firm-level production networks By Christian Diem; Andr\'as Borsos; Tobias Reisch; J\'anos Kert\'esz; Stefan Thurner

  1. By: Hamed Amini; Zhongyuan Cao; Andreea Minca; Agn\`es Sulem
    Abstract: We study multidimensional Cram\'er-Lundberg risk processes where agents, located on a large sparse network, receive losses form their neighbors. To reduce the dimensionality of the problem, we introduce classification of agents according to an arbitrary countable set of types. The ruin of any agent triggers losses for all of its neighbours. We consider the case when the loss arrival process induced by the ensemble of ruined agents follows a Poisson process with general intensity function that scales with the network size. When the size of the network goes to infinity, we provide explicit ruin probabilities at the end of the loss propagation process for agents of any type. These limiting probabilities depend, in addition to the agents' types and the network structure, on the loss distribution and the loss arrival process. For a more complex risk processes on open networks, when in addition to the internal networked risk processes the agents receive losses from external users, we provide bounds on ruin probabilities.
    Date: 2023–02
  2. By: Ji Su Kim (Max Planck Institute for Demographic Research, Rostock, Germany); Soazic Elise Wang Sonne (Max Planck Institute for Demographic Research, Rostock, Germany); Kiran Garimella; André Grow (Max Planck Institute for Demographic Research, Rostock, Germany); Ingmar G. Weber; Emilio Zagheni (Max Planck Institute for Demographic Research, Rostock, Germany)
    Abstract: As online social activities have become increasingly important for people’s lives and well-being, understanding how migrants integrate into online spaces is crucial for providing a more complete picture of integration processes. We curate a high-quality data set to quantify patterns of new online social connections among immigrants in the United States. Specifically, we focus on Twitter, and leverage the unique features of these data, in combination with a propensity score matching technique, to isolate the effects of migration events on social network formation. The results indicate that migration events led to an expansion of migrants' networks of friends on Twitter in the destination country, relative to those of users who had similar characteristics, but who did not move. We found that male migrants between 19 and 29 years old who actively posted more tweets in English after migration also tended to have more local friends after migration compared to other demographic group, which indicates that migrants' demographic characteristics and language skills can affect their level of integration. We also observed that the percentage of migrants' friends who were from their country of origin decreased in the first few years after migration, and increased again in later years. Finally, unlike for migrants' friends networks, which were under their control, we did not find any evidence that migration events expanded migrants' networks of followers in the destination country. While following users on Twitter in theory is not a geographically constrained process, our work shows that offline (re)location plays a significant role in the formation of online networks.
    Keywords: America, World, immigrants, immigration, integration, social network
    JEL: J1 Z0
    Date: 2023
  3. By: Tianyuan Zhang (CGI - Centre Génie Industriel - IMT Mines Albi - IMT École nationale supérieure des Mines d'Albi-Carmaux - IMT - Institut Mines-Télécom [Paris]); Jiayao Li (CGI - Centre Génie Industriel - IMT Mines Albi - IMT École nationale supérieure des Mines d'Albi-Carmaux - IMT - Institut Mines-Télécom [Paris]); Frederick Benaben (CGI - Centre Génie Industriel - IMT Mines Albi - IMT École nationale supérieure des Mines d'Albi-Carmaux - IMT - Institut Mines-Télécom [Paris])
    Abstract: Cascading effects describe risk interdependencies, whereby the occurrence of one risk may trigger one or more risks with potential propagation chains in complex systems. In this study, on the basis of a formalized model namely danger-risk-consequence chain, a generic simulation framework is proposed to characterize risk causal processes and cascading effects within collaborative networks. Risk-related components and the causal relationships between them are visualized by abstractly representing the instantaneous state of the considered collaborative network as a directed graph. Furthermore, the simulation of trajectories of the state evolution over time is realized by knowledge-driven automatic inference of causal chains and propagation chains, thus enabling the tracing of cascading effects within complex systems. The presented simulation framework provides a solid foundation for a systemic understanding of risks, which implies an innovative tool that helps decision-makers to identify, prevent and mitigate cascading effects within collaborative networks (e.g., supply chains).
    Keywords: Simulation, Cascading effect, Risk interdependency, Collaborative network, Framework
    Date: 2022–09–19
  4. By: Gu, Xin (Georgia Institute of Technology); Li, Haizheng (Georgia Tech)
    Abstract: We study peer effects in online training participation using unique data from a large-scale online teacher training program. The platform data allow us to observe the accurate duration of attendance for every individual-lecture pair. We classify peer groups as close peers, local peers, and global peers based on their relationships. By controlling for unobserved heterogeneity, we find positive effects of close and local peer appearance on trainees' joining a lecture and on their length of stay in the lecture. However, global peers generate a negative but economically insignificant impact. Peer effects differ by group and increase with the relationship closeness. Using the survey data, we investigate the mechanisms of peer influences and find that social interactions facilitate online peer effects. Peer pressure and reputation concerns also help explain our findings. Our results shed new light on how peer effects can be utilized to improve the effectiveness of online learning.
    Keywords: peer effects, online training
    JEL: I21 J24 M53
    Date: 2023–02
  5. By: Costas Arkolakis; Federico Huneeus; Yuhei Miyauchi
    Abstract: We use new theory and data to study how firms endogenously form production networks across regions and countries. Supplier and buyer relationships form depending on firms' productivity and geographic location. We characterize the normative and positive properties of the spatial distribution of economic activity and welfare in general equilibrium. We calibrate the model using domestic and international firm-to-firm trade data from Chile. Both iceberg trade costs and search and matching frictions are important for aggregate trade flows and production networks. Endogenous formation of production networks leads to larger and more dispersed effects of international and intra-national trade cost shocks.
    JEL: F10 R13
    Date: 2023–02
  6. By: Johannes Stangl; Andr\'as Borsos; Christian Diem; Tobias Reisch; Stefan Thurner
    Abstract: A rapid decarbonization of the economy requires a massive reconfiguration of its underlying production networks. To reduce emissions significantly, many firms need to change production processes, which has major impacts on practically all supply chains. This restructuring process might cause considerable social distress, e.g. in the form of unemployment, if companies have to close down. Here, we use a unique dataset of the entire firm-level production network of a European economy and develop a network-theory-based measure to estimate the systemic social relevance of every single firm. It enables us to estimate the expected direct and indirect job losses in the supply chain triggered by every firm's default. For the largest CO2 emitting firms we link this measure of social relevance to their emissions. We identify firms with low social relevance and high emissions as potential decarbonization leverage points. We compare various decarbonization strategies by simultaneously capturing the social and environmental impact under the assumption that specific sets of firms would no longer produce. We find that a strategy based on the identified decarbonization leverage points could lead to a 20% reduction of CO2 emissions while putting 2% of jobs at risk. In contrast, targeting the largest emitters first, without considering their social relevance, results in 33% of jobs being at risk for comparable emission savings. Our results indicate that supply-chain sensitive CO2 taxation might reduce the social costs of the green transition considerably.
    Date: 2023–02
  7. By: Christian Diem; Andr\'as Borsos; Tobias Reisch; J\'anos Kert\'esz; Stefan Thurner
    Abstract: To estimate the reaction of economies to political interventions or external disturbances, input-output (IO) tables -- constructed by aggregating data into industrial sectors -- are extensively used. However, economic growth, robustness, and resilience crucially depend on the detailed structure of non-aggregated firm-level production networks (FPNs). Due to non-availability of data little is known about how much aggregated sector-based and detailed firm-level-based model-predictions differ. Using a nearly complete nationwide FPN, containing 243, 399 Hungarian firms with 1, 104, 141 supplier-buyer-relations we self-consistently compare production losses on the aggregated industry-level production network (IPN) and the granular FPN. For this we model the propagation of shocks of the same size on both, the IPN and FPN, where the latter captures relevant heterogeneities within industries. In a COVID-19 inspired scenario we model the shock based on detailed firm-level data during the early pandemic. We find that using IPNs instead of FPNs leads to errors up to 37% in the estimation of economic losses, demonstrating a natural limitation of industry-level IO-models in predicting economic outcomes. We ascribe the large discrepancy to the significant heterogeneity of firms within industries: we find that firms within one sector only sell 23.5% to and buy 19.3% from the same industries on average, emphasizing the strong limitations of industrial sectors for representing the firms they include. Similar error-levels are expected when estimating economic growth, CO2 emissions, and the impact of policy interventions with industry-level IO models. Granular data is key for reasonable predictions of dynamical economic systems.
    Date: 2023–02

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