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on Network Economics |
| By: | Giorgio Fabbri; Silvia Faggian; Giuseppe Freni |
| Abstract: | This study examines the dynamics of capital stocks distributed among several nodes, representing different sites of production and connected via a weighted, directed network. The network represents the externalities or spillovers that the production in each node generates on the capital stock of other nodes. A regulator decides to designate some of the nodes for the production of a consumption good to maximize a cumulative utility from consumption. It is demonstrated how the optimal strategies and stocks depend on the productivity of the resource sites and the structure of the connections between the sites. The best locations to host production of the consumption good are identified per the model’s parameters and correspond to the least central (in the sense of eigenvector centrality) nodes of a suitably redefined network that combines both flows between nodes and the nodes’ productivity. |
| Keywords: | Capital Allocation, Production Externalities, Network Spillovers, Economic Centrality Measures. |
| JEL: | C61 D62 O41 R12 |
| Date: | 2023–05 |
| URL: | https://d.repec.org/n?u=RePEc:gbl:wpaper:2024-04&r= |
| By: | Giorgio Fabbri; Silvia Faggian; Giuseppe Freni |
| Abstract: | This study examines the dynamics of the exploitation of a natural resource distributed among and flowing between several nodes connected via a weighted, directed network. The network represents the locations and interactions of the resource nodes. A regulator decides to designate some of the nodes as natural reserves where no exploitation is allowed. The remaining nodes are assigned (one-to-one) to players, who exploit the resource at the node. It is demonstrated how the equilibrium exploitation and resource stocks depend on the productivity of the resource sites, the structure of the connections between the sites, and the number and preferences of the agents. The best locations to host nature reserves are identified per the model’s parameters and correspond to the most central (in the sense of eigenvector centrality) nodes of a suitably redefined network that considers the nodes’ productivity. |
| Keywords: | Harvesting, Spatial Models, Differential Games, Nature Reserves |
| JEL: | Q20 Q28 R11 C73 |
| Date: | 2023–05 |
| URL: | https://d.repec.org/n?u=RePEc:gbl:wpaper:2024-03&r= |
| By: | Alastair Langtry; Sarah Taylor; Yifan Zhang |
| Abstract: | This paper studies the general class of games where agents: (1) are embedded on a network, (2) have two possible actions, and (3) these actions are strategic complements. We use a measure of network cohesiveness -- the k-core -- to provide a novel characterisation of the equilibria. After transforming the network appropriately, the k-core fully describes both the minimal and maximal equilibria, and also provides a partial characterisation of all others. This framework is also the binary action version of the large class of network games with strategic complements and continuous actions. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.04540&r= |
| By: | Aristide Houndetoungan |
| Abstract: | This paper develops a micro-founded peer effect model for count responses using a game of incomplete information. The model incorporates heterogeneity in peer effects through agents' groups based on observed characteristics. Parameter identification is established using the identification condition of linear models, which relies on the presence of friends' friends who are not direct friends in the network. I show that this condition extends to a large class of nonlinear models. The model parameters are estimated using the nested pseudo-likelihood approach, controlling for network endogeneity. I present an empirical application on students' participation in extracurricular activities. I find that females are more responsive to their peers than males, whereas male peers do not influence male students. An easy-to-use R packag--named CDatanet--is available for implementing the model. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.17290&r= |
| By: | Walter Farkas (University of Zurich - Department Finance; Swiss Finance Institute; ETH Zürich); Patrick Lucescu (University of Zurich - Department of Finance) |
| Abstract: | This paper develops a simplified agent-based model to investigate the dynamics of risk transfer and its implications for systemic risk within financial networks, focusing specifically on Credit Default Swaps (CDSs) as instruments of risk allocation among banks and firms. Unlike broader models that incorporate multiple types of economic agents, our approach explicitly targets the interactions between banks and firms across three markets: credit, interbank loans, and CDSs. This model diverges from the frameworks established by Leduc, Poledna, and Thurner (2016) and Poledna and Thurner (2016) by simplifying the agent structure, which allows for more focused calibration to empirical data—specifically, a sample of Swiss banks—and enhances interpretability for regulatory use. Our analysis centers around two control variables, CDSc and CDSn, which modulate the likelihood of institutions participating in covered and naked CDS transactions, respectively. This approach allows us to explore the network’s behavior under varying levels of interconnectedness and differing magnitudes of deposit shocks. Our results indicate that the network can withstand minor shocks, but higher levels of CDS engagement significantly increase variance and kurtosis in equity returns, signaling heightened instability. This effect is amplified during severe shocks, suggesting that CDSs, instead of mitigating risk, propagate systemic risk, particularly in highly interconnected networks. These findings underscore the need for regulatory oversight to manage risk concentration and ensure financial stability. |
| Keywords: | Systemic Risk, Agent-Based Modeling, Financial Networks, Risk Transfer, Network Interconnectedness, Credit Default Swaps |
| JEL: | C63 D85 G01 G21 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2432&r= |
| By: | Mathieu Lambotte (Univ Rennes, CNRS, CREM – UMR6211, F-35000 Rennes France) |
| Abstract: | I introduce asymmetry in the analysis of peer effects, microfounded on a network game for a binary outcome. Indeed, overdoing and underdoing relatively to the social norm might lead to asymmetric social penalties. The extent and direction of this asymmetry depends on the behavior under scrutiny. I develop conditions under which this network game results in an unique Bayes-Nash equilibrium depending on rational expectations about peers’ behavior and propose an estimation strategy based on an nested fixed point maximum likelihood estimator. The model is brought to the data with an application to smoking and alcohol drinking behaviors of high-school students in the United States. |
| Keywords: | peer effects, asymmetry, social norm, binary outcome, rational expectations |
| JEL: | C31 C35 C57 C72 D84 R41 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:tut:cremwp:2024-05&r= |
| By: | Allmis, Patrick; Pin, Paolo; Vega-Redondo, Fernando |
| Abstract: | We propose a simple micro-founded model of trading with ex-ante asymmetric information similar to one proposed by Kyle (1985) in which the equilibrium price is fully revealing under rational expectations. We analyze under which conditions a privately informed trader may want to share her information with other traders for free. Despite the strictly competitive setupand conventional wisdom, we show that there is a unique separating equilibrium in which the informed trader reveals some signals and conceals others. A consequence of this is that the price need not be fully revealing of the aggregate information in the market (even if traders are risk neutral), which in turn has welfare implications on the distribution of the social surplusat equilibrium. We establish these results for a context where the pattern of communication among traders is restricted by a given social network, studying as well what network arises when links are established endogenously. |
| Keywords: | Information Sharing; Market Efficiency; Financial Markets; Information Aggregation; Communication Network; Network Formation |
| Date: | 2024–06–10 |
| URL: | https://d.repec.org/n?u=RePEc:cte:werepe:43966&r= |
| By: | Deer, Lachlan; Hsieh, Chih-Sheng; König, Michael D.; Vega-Redondo, Fernando |
| Abstract: | We study a dynamic model of collective action in which agents are connected by a social network. Our approach highlights the importance of communication in this problem and conceives that network &- which is continuously evolving &- as providing the channel through which agents not only interact but also communicate. We consider two alternative scenarios that differ only on how agents form their expectations: while in the "benchmark" context agents are completely informed, in the alternative one their expectations are formed through a combination of local observation and sociallearning à la DeGroot. We completely characterize the long-run behavior of the system in both cases and show that only in the latter scenario (arguably the most realistic) there is a significant long-run probability that agents eventually achieve collective action within a meaningful time scale. We suggest that this sheds light on the puzzle of how large populations can coordinate on globallydesired outcomes. Finally, we illustrate the empirical potential of the model by showing that it can be efficiently estimated for the Egyptian Arab Spring using large-scale cross-sectional data from Twitter. This estimation exercise also suggests that, in this instance, network-based social learning played a leading role in the process underlying collective action. |
| Keywords: | Collective Action; Networks; Coordination; Social Protests; Degroot; Social Learning |
| Date: | 2024–06–06 |
| URL: | https://d.repec.org/n?u=RePEc:cte:werepe:43961&r= |
| By: | Elena Ashtari Tafti; Mimosa Distefano; Tetyana Surovtseva |
| Abstract: | We use Italian Social Security data to study how the gender composition of a worker's professional network influences their career development. By exploiting variation within firms, occupations, and labor market entry cohorts, we find that young women starting their careers alongside a higher share of female peers experience lower wage growth, fewer promotions and increased transitions into non-employment. In contrast, male workers appear unaffected. The analysis reveals that these gender-specific effects are largely driven by structural differences in the networks of men and women. Networks predominantly composed of women appear to be less effective in the labor market. Women, who experience higher attrition and lower promotion rates, have fewer connections to employment opportunities, and their connections tend to be less valuable. When accounting for these differences, we find that connections among female peers offer a crucial safety net during adverse employment shocks. Our findings highlight the critical role of early-career peers and provide a new perspective on the barriers to career advancement for women |
| Keywords: | gender peer effects, networks, labor market entrants, career progression |
| Date: | 2024–06–14 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2008&r= |
| By: | Imran Ansari; Charu Sharma; Akshay Agrawal; Niteesh Sahni |
| Abstract: | This paper highlights the significance of mesoscale structures, particularly the core-periphery structure, in financial networks for portfolio optimization. We build portfolios of stocks belonging to the periphery part of the Planar maximally filtered subgraphs of the underlying network of stocks created from Pearson correlations between pairs of stocks and compare its performance with some well-known strategies of Pozzi et. al. hinging around the local indices of centrality in terms of the Sharpe ratio, returns and standard deviation. Our findings reveal that these portfolios consistently outperform traditional strategies and further the core-periphery profile obtained is statistically significant across time periods. These empirical findings substantiate the efficacy of using the core-periphery profile of the stock market network for both inter-day and intraday trading and provide valuable insights for investors seeking better returns. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.12993&r= |
| By: | Tadao Hoshino; Takahide Yanagi |
| Abstract: | This paper proposes a statistical inference method for assessing treatment effects with dyadic data. Under the assumption that the treatments follow an exchangeable distribution, our approach allows for the presence of any unobserved confounding factors that potentially cause endogeneity of treatment choice without requiring additional information other than the treatments and outcomes. Building on the literature of graphon estimation in network data analysis, we propose a neighborhood kernel smoothing method for estimating dyadic average treatment effects. We also develop a permutation inference method for testing the sharp null hypothesis. Under certain regularity conditions, we derive the rate of convergence of the proposed estimator and demonstrate the size control property of our test. We apply our method to international trade data to assess the impact of free trade agreements on bilateral trade flows. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.16547&r= |
| By: | Kensuke Sakamoto |
| Abstract: | This paper addresses the sample selection problem in panel dyadic regression analysis. Dyadic data often include many zeros in the main outcomes due to the underlying network formation process. This not only contaminates popular estimators used in practice but also complicates the inference due to the dyadic dependence structure. We extend Kyriazidou (1997)'s approach to dyadic data and characterize the asymptotic distribution of our proposed estimator. The convergence rates are $\sqrt{n}$ or $\sqrt{n^{2}h_{n}}$, depending on the degeneracy of the H\'{a}jek projection part of the estimator, where $n$ is the number of nodes and $h_{n}$ is a bandwidth. We propose a bias-corrected confidence interval and a variance estimator that adapts to the degeneracy. A Monte Carlo simulation shows the good finite performance of our estimator and highlights the importance of bias correction in both asymptotic regimes when the fraction of zeros in outcomes varies. We illustrate our procedure using data from Moretti and Wilson (2017)'s paper on migration. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.17787&r= |