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on Network Economics |
| By: | Comola, Margherita (Paris School of Economics); Rusinowska, Agnieszka (Paris School of Economics); Villeval, Marie Claire (CNRS, GATE) |
| Abstract: | We experimentally investigate how players with opposing views compete for influence through strategic targeting in networks. We varied the network structure, the relative influence of the opponent, and the heterogeneity of the nodes' initial opinions. Although most players adopted a best-response strategy based on their relative influence, we also observed behaviors deviating from this strategy, such as the tendency to target central nodes and avoid nodes targeted by the opponent. Targeting is also affected by affinity and opposition biases, the strength of which depends on the distribution of initial opinions. |
| Keywords: | network, influence, targeting, competition, laboratory experiment |
| JEL: | C91 D85 D91 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17315 |
| By: | Yulia Evsyukova; Felix Rusche; Wladislaw Mill |
| Abstract: | We assess the impact of discrimination on Black individuals’ job networks across the U.S. using a two-stage field experiment with 400+ fictitious LinkedIn profiles. In the first stage, we vary race via AI-generated images only and find that Black profiles’ connection requests are 13 percent less likely to be accepted. Based on users’ CVs, we find widespread discrimination across social groups. In the second stage, we exogenously endow Black and White profiles with the same networks and ask connected users for career advice. We find no evidence of direct discrimination in information provision. However, when taking into account differences in the composition and size of networks, Black profiles receive substantially fewer replies. Our findings suggest that gatekeeping is a key driver of Black-White disparities. |
| Keywords: | Discrimination, Job Networks, Labor Markets, Field Experiment |
| JEL: | J71 J15 C93 J46 D85 |
| Date: | 2023–12 |
| URL: | https://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2023_482v2 |
| By: | Li, Mengxue (Université catholique de Louvain, LIDAM/ISBA, Belgium); von Sachs, Rainer (Université catholique de Louvain, LIDAM/ISBA, Belgium); Pircalabelu, Eugen (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | Recent interest has emerged in community detection for dynamic networks which are observed along a trajectory of points in time. In this paper, we present a time-varying degree-corrected stochastic block model to fit a dynamic network which allows evolving heterogeneity in the degrees of nodes within a community over time. Considering the influence of the varying time window on the aggregation of network information from different time points, in the parameter estimation, we propose a smoothing-based method to recover time-varying degree parameters and communities. We also provide rates of consistency of our smoothed estimators for degree parameters and communities using a time-localised profile- likelihood approach. Extensive simulation studies and applications to two different real data sets complete our work. |
| Keywords: | Dynamic network ; Community detection ; Time-localised profile-likelihood ; Nonparametric curve estimation |
| Date: | 2024–04–21 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2024014 |
| By: | Tizié Bene (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique); Yann Bramoullé (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique); Frédéric Deroïan (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We study how altruism networks affect the demand for formal insurance. Agents with CARA utilities are connected through a network of altruistic relationships. Incomes are subject to a common shock and to a large individual shock, generating heterogeneous damages. Agents can buy formal insurance to cover the common shock, up to a coverage cap. We find that ex-post altruistic transfers induce interdependence in ex-ante formal insurance decisions. We characterize the Nash equilibria of the insurance game and show that agents act as if they are trying to maximize the expected utility of a representative agent with average damages. Altruism thus tends to increase demand of low-damage agents and to decrease demand of high-damage agents. Its aggregate impact depends on the interplay between demand homogenization, the zero lower bound and the coverage cap. We find that aggregate demand is higher with altruism than without altruism at low prices and lower at high prices. Nash equilibria are constrained Pareto efficient. |
| Keywords: | Formal insurance, Informal transfers, Altruism networks |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04717990 |
| By: | Lucas Gomes; Jannis Kueck; Mara Mattes; Martin Spindler; Alexey Zaytsev |
| Abstract: | Collusion is a complex phenomenon in which companies secretly collaborate to engage in fraudulent practices. This paper presents an innovative methodology for detecting and predicting collusion patterns in different national markets using neural networks (NNs) and graph neural networks (GNNs). GNNs are particularly well suited to this task because they can exploit the inherent network structures present in collusion and many other economic problems. Our approach consists of two phases: In Phase I, we develop and train models on individual market datasets from Japan, the United States, two regions in Switzerland, Italy, and Brazil, focusing on predicting collusion in single markets. In Phase II, we extend the models' applicability through zero-shot learning, employing a transfer learning approach that can detect collusion in markets in which training data is unavailable. This phase also incorporates out-of-distribution (OOD) generalization to evaluate the models' performance on unseen datasets from other countries and regions. In our empirical study, we show that GNNs outperform NNs in detecting complex collusive patterns. This research contributes to the ongoing discourse on preventing collusion and optimizing detection methodologies, providing valuable guidance on the use of NNs and GNNs in economic applications to enhance market fairness and economic welfare. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.07091 |
| By: | FUJII Daisuke |
| Abstract: | Recent events of the Russian invasion of Ukraine and the US-China decoupling have shown that key trade policies today are shaped by geopolitical risks and economic security concerns. In Japan, economic security in increasingly complex global supply chains is also being discussed as an important policy theme, though quantitative evidence remains scarce. This paper aims to quantify the impact of trade disruptions with China on the Japanese economy. To do so, I develop a general equilibrium model of production networks with international trade, which incorporates non-unitary elasticity of substitution across intermediate inputs. The model is calibrated using large-scale firm-level network data from Japan. The aggregate impact of trade disruption is substantial in the short run but becomes milder in the long run. If both exports and imports with China decline by 90%, real GDP is projected to drop by 7% within a year. Additionally, import disruptions cause more severe damage than export disruptions. There is significant sectoral heterogeneity in the negative impact of trade disruptions, depending on sectoral exposure to trade, the share of intermediate inputs, and position within production networks. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:24073 |
| By: | Jacob Morrier; Amine Mahmassani; R. Michael Alvarez |
| Abstract: | Toxicity is a widespread phenomenon in competitive online video games. In addition to its direct undesirable effects, there is a concern that toxicity can spread to others, amplifying the harm caused by a single player's misbehavior. In this study, we estimate whether and to what extent a player's toxic speech spreads, causing their teammates to behave similarly. To this end, we analyze proprietary data from the free-to-play first-person action game Call of Duty: Warzone. We formulate and implement an instrumental variable identification strategy that leverages the network of interactions among players across matches. Our analysis reveals that all else equal, all of a player's teammates engaging in toxic speech increases their probability of engaging in similar behavior by 26.1 to 30.3 times the average player's likelihood of engaging in toxic speech. These findings confirm the viral nature of toxicity, especially toxic speech, in competitive online video games. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.00978 |