nep-net New Economics Papers
on Network Economics
Issue of 2021‒11‒01
five papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Free Riding in Networks By Markus Kinateder; Luca Paolo Merlino
  2. Information and Immigrant Settlement By Toman Barsbai; Victoria Licuanan; Andreas Steinmayr; Erwin Tiongson; Dean Yang
  3. You Are Who You Eat With: Academic Peer Effects from School Lunch Lines By Presler, Jonathan
  4. Forecasting Financial Market Structure from Network Features using Machine Learning By Douglas Castilho; Tharsis T. P. Souza; Soong Moon Kang; Jo\~ao Gama; Andr\'e C. P. L. F. de Carvalho
  5. Cyber contagion: impact of the network structure on the losses of an insurance portfolio By Caroline Hillairet; Olivier Lopez; Louise d'Oultremont; Brieuc Spoorenberg

  1. By: Markus Kinateder; Luca Paolo Merlino
    Abstract: Players allocate their budget to links, a local public good and a private good. A player links to free ride on others' public good provision. We derive sufficient conditions for the existence of a Nash equilibrium. In equilibrium, large contributors link to each other, while others link to them. Poorer players can be larger contributors if linking costs are sufficiently high. In large societies, free riding reduces inequality only in networks in which it is initially low; otherwise, richer players benefit more, as they can afford more links. Finally, we study the policy implications, deriving income redistribution that increases welfare and personalized prices that implement the efficient solution.
    Date: 2021–10
  2. By: Toman Barsbai; Victoria Licuanan; Andreas Steinmayr; Erwin Tiongson; Dean Yang
    Abstract: We study a randomly-assigned program providing information on U.S. settlement for new Filipino immigrants. The intervention, a 2.5-hour pre-departure training and an accompanying paper handbook, has no effect on employment, settlement, and subjective wellbeing, but leads immigrants to acquire substantially fewer social network connections. We rationalize these findings with a simple model, showing that information and social network links are substitutes under reasonable assumptions. Consistent with the model, the treatment reduces social network links more when costs of acquiring network links are lower. Offsetting reductions in the acquisition of social network connections can hence reduce the effectiveness of information interventions.
    Keywords: Immigrant integration, social networks, imperfect information, multiple hypothesis testing
    JEL: D83 F22
    Date: 2021
  3. By: Presler, Jonathan (Sinquefield Center for Applied Economic Research, Saint Louis University)
    Abstract: Using daily lunch transaction data from NYC public schools, I determine which students frequently stand next to one another in the lunch line. I use this `revealed' friendship network to estimate academic peer effects in elementary school classrooms, improving on previous work by defining not only where social connections exist, but the relative strength of these connections. Equally weighting all peers in a reference group assumes that all peers are equally important and may bias estimates by underweighting important peers and overweighting unimportant peers. I find that students who eat together are important influencers of one another's academic performance, with stronger effects in math than in reading. Further exploration of the mechanisms supports my claim that these are friendship networks. I also compare the influence of friends from different periods in the school year and find that connections occurring around standardized testing dates are most influential on test scores.
    Keywords: Peer effect; network; education; lunch line
    JEL: C31 I21
    Date: 2021–06–01
  4. By: Douglas Castilho; Tharsis T. P. Souza; Soong Moon Kang; Jo\~ao Gama; Andr\'e C. P. L. F. de Carvalho
    Abstract: We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of asset price returns across company constituents of major global market indices. We provide empirical evidence using three different network filtering methods to estimate market structure, namely Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST) and Dynamic Threshold Networks (DTN). Experimental results show that the proposed model can forecast market structure with high predictive performance with up to $40\%$ improvement over a time-invariant correlation-based benchmark. Non-pair-wise correlation features showed to be important compared to traditionally used pair-wise correlation measures for all markets studied, particularly in the long-term forecasting of stock market structure. Evidence is provided for stock constituents of the DAX30, EUROSTOXX50, FTSE100, HANGSENG50, NASDAQ100 and NIFTY50 market indices. Findings can be useful to improve portfolio selection and risk management methods, which commonly rely on a backward-looking covariance matrix to estimate portfolio risk.
    Date: 2021–10
  5. By: Caroline Hillairet (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique); Olivier Lopez (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UP - Université de Paris); Louise d'Oultremont; Brieuc Spoorenberg
    Abstract: In this paper, we provide a model that aims to describe the impact of a massive cyber attack on an insurance portfolio, taking into account the structure of the network. Due to the contagion, such an event can rapidly generate consequent damages, and mutualization of the losses may not hold anymore. The composition of the portfolio should therefore be diversified enough to prevent or reduce the impact of such events, with the difficulty that the relationships between actor is difficult to assess. Our approach consists in introducing a multi-group epidemiological model which, apart from its ability to describe the intensity of connections between actors, can be calibrated from a relatively small amount of data, and through fast numerical procedures. We show how this model can be used to generate reasonable scenarios of cyber events, and investigate the response to different types of attacks or behavior of the actors, allowing to quantify the benefit of an efficient prevention policy.
    Keywords: Cyber insurance,cyber risk,compartmental models,multi-SIR,network structures
    Date: 2021–10–20

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