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on Network Economics |
| By: | Valentina Macchiati; Piero Mazzarisi; Diego Garlaschelli |
| Abstract: | Networks of financial exposures are the key propagators of risk and distress among banks, but their empirical structure is not publicly available because of confidentiality. This limitation has triggered the development of methods of network reconstruction from partial, aggregate information. Unfortunately, even the best methods available fail in replicating the number of directed cycles, which on the other hand play a crucial role in determining graph spectra and hence the degree of network stability and systemic risk. Here we address this challenge by exploiting the hypothesis that the statistics of higher-order cycles is strongly constrained by that of the shortest ones, i.e. by the amount of dyads with reciprocated links. First, we provide a detailed analysis of link reciprocity on the e-MID dataset of Italian banks, finding that correlations between reciprocal links systematically increase with the temporal resolution, typically changing from negative to positive around a timescale of up to 50 days. Then, we propose a new network reconstruction method capable of enforcing, only from the knowledge of aggregate interbank assets and liabilities, both a desired sparsity and a desired link reciprocity. We confirm that the addition of reciprocity dramatically improves the prediction of several structural and spectral network properties, including the largest real eigenvalue and the eccentricity of the elliptical distribution of the other eigenvalues in the complex plane. These results illustrate the importance of correctly addressing the temporal resolution and the resulting level of reciprocity in the reconstruction of financial networks. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.11136&r=net |
| By: | Yu Awaya; Vijay Krishna |
| Abstract: | An informed planner wishes to spread information among a group of agents in order to induce efficient coordination -- say the adoption of a new technology with positive externalities. The agents are connected via a social network. The planner informs a seed and then the information spreads via the network. While the structure of the network affects the rate of diffusion, we show that the rate of adoption is the same for all acyclic networks. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.05276&r=net |
| By: | Sunday Akukodi Ugwu; Shazia'Ayn Babul; Sofia Medina |
| Abstract: | The mini-project models propagation of shocks, in time point, through links in connected banks. In particular, financial network of 100 banks out of which 15 are shocked to default (that is, 85.00% of the banks are solvent) is modelled using Erdos and Renyi network -- directed, weighted and randomly generated network. Shocking some banks in a financial network implies removing their assets and redistributing their liabilities to other connected ones in the network. The banks are nodes and two ranges of probability values determine tendency of having a link between a pair of banks. Our major finding shows that the ranges of probability values and banks' percentage solvency have positive correlation. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.08071&r=net |
| By: | Cornelius Fritz; Co-Pierre Georg; Angelo Mele; Michael Schweinberger |
| Abstract: | Modern software development involves collaborative efforts and reuse of existing code, which reduces the cost of developing new software. However, reusing code from existing packages exposes coders to vulnerabilities in these dependencies. We study the formation of dependency networks among software packages and libraries, guided by a structural model of network formation with observable and unobservable heterogeneity. We estimate costs, benefits, and link externalities of the network of 696, 790 directed dependencies between 35, 473 repositories of the Rust programming language using a novel scalable algorithm. We find evidence of a positive externality exerted on other coders when coders create dependencies. Furthermore, we show that coders are likely to link to more popular packages of the same software type but less popular packages of other types. We adopt models for the spread of infectious diseases to measure a package's systemicness as the number of downstream packages a vulnerability would affect. Systemicness is highly skewed with the most systemic repository affecting almost 90% of all repositories only two steps away. Lastly, we show that protecting only the ten most important repositories reduces vulnerability contagion by nearly 40%. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.13375&r=net |
| By: | Sanz, Leandro (Ohio State U) |
| Abstract: | This study uses a novel dataset of over 11, 000 foreign suppliers to U.S. manufacturers to investigate the impact of supply network fragility on corporate policies. The scarcity of suppliers offering specialized inputs emerges as a key driver of fragility. Both theoretical and empirical evidence indicate that firms with fragile supply networks maintain more input inventories, less cash, and higher leverage. Moreover, plausible exogenous variation in fragility from technology adoption and disruptions supports a causal interpretation of the results. My findings indicate that because specialized inputs lack a spot market post-disruptions, firms with fragile supply networks favor operational over financial hedging. |
| JEL: | F23 G31 G32 L23 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:ohidic:2023-25&r=net |
| By: | Gaurav Chiplunkar; Erin M. Kelley; Gregory V. Lane |
| Abstract: | We study how job-seekers share information about jobs within their social network, and its implications for firms. We randomly increase the amount of competition for a job and find that job-seekers are: (i) less likely to share information about the job with their peers; and (ii) choose to selectively share it with fewer higher ability peers. This lowers the quality of applicants received by firms, subsequent hires made, and performance on the job — suggesting that firms who rely on social networks to disseminate job information may see lower quality applicants than expected for their most competitive positions. While randomly offering higher wages attracts better talent, it is not able to fully overcome these strategic disincentives in information sharing |
| JEL: | L14 M51 O12 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32171&r=net |
| By: | Martin Hoefer; Carmine Ventre; Lisa Wilhelmi |
| Abstract: | The recent banking crisis has again emphasized the importance of understanding and mitigating systemic risk in financial networks. In this paper, we study a market-driven approach to rescue a bank in distress based on the idea of claims trading, a notion defined in Chapter 11 of the U.S. Bankruptcy Code. We formalize the idea in the context of financial networks by Eisenberg and Noe. For two given banks v and w, we consider the operation that w takes over some claims of v and in return gives liquidity to v to ultimately rescue v. We study the structural properties and computational complexity of decision and optimization problems for several variants of claims trading. When trading incoming edges of v, we show that there is no trade in which both banks v and w strictly improve their assets. We therefore consider creditor-positive trades, in which v profits strictly and w remains indifferent. For a given set C of incoming edges of v, we provide an efficient algorithm to compute payments by w that result in maximal assets of v. When the set C must also be chosen, the problem becomes weakly NP-hard. Our main result here is a bicriteria FPTAS to compute an approximate trade. The approximate trade results in nearly the optimal amount of assets of v in any exact trade. Our results extend to the case in which banks use general monotone payment functions and the emerging clearing state can be computed efficiently. In contrast, for trading outgoing edges of v, the goal is to maximize the increase in assets for the creditors of v. Notably, for these results the characteristics of the payment functions of the banks are essential. For payments ranking creditors one by one, we show NP-hardness of approximation within a factor polynomial in the network size, when the set of claims C is part of the input or not. Instead, for proportional payments, our results indicate more favorable conditions. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.13627&r=net |
| By: | Antoine Berthou; Antton Haramboure; Lea Samek |
| Abstract: | This paper conducts an in-depth mapping of global value chain (GVC) vulnerabilities, using granular product-level trade data to identify vulnerable products with limited suppliers and substitutability. The study reveals that, in OECD countries, approximately 8% of foreign-sourced intermediate products are vulnerable, with about 50 products identified as highly vulnerable, particularly in the pharmaceutical, mining, and manufacturing sectors. The paper also introduces a quantitative framework for simulating supply shock transmission from upstream suppliers to downstream industries over the short and medium term. This framework leverages unique data that combine Inter-Country Input-Output with detailed product-level trade data from Comtrade. Through simulation exercises, the paper highlights the role of supplier concentration and geography in shock transmissions, as well as the effectiveness of policies in mitigating these impacts. This novel cross-country assessment of GVC disruptions provides new insights on how to manage supply chains in a global economy subject to multiple risks. |
| Keywords: | Global Value Chains, International trade, Resilience |
| JEL: | F14 F68 L52 |
| Date: | 2024–03–15 |
| URL: | http://d.repec.org/n?u=RePEc:oec:stiaaa:2024/02-en&r=net |
| By: | Brad Cannon; David Hirshleifer; Joshua Thornton |
| Abstract: | Using friendship data from Facebook, we study the effects of three aspects of social capital on household financial behavior. We find that the most important measure of social capital in explaining stock market and saving participation is Economic Connectedness, defined as the fraction of one’s social network with high socioeconomic status. One standard-deviation greater Economic Connectedness is associated with 2.9% greater stock market participation and 5.0% greater saving participation. Compared to Cohesiveness or Civic Engagement, Economic Connectedness explains more than 6 times the variation in stock market participation and more than 4 times the variation in saving participation. Using data on nonlocal friendships, we provide evidence supporting a causal link between household financial behavior and the income of one's friends. Furthermore, we provide evidence that greater opportunities for social interaction with wealthy individuals is associated with increased stock market and saving participation. |
| JEL: | D14 D15 D63 G4 G40 G41 G5 G50 G51 I3 I30 I31 I38 O16 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32186&r=net |
| By: | Catayoun Azarm; Erman Acar; Mickey van Zeelt |
| Abstract: | Online transaction fraud presents substantial challenges to businesses and consumers, risking significant financial losses. Conventional rule-based systems struggle to keep pace with evolving fraud tactics, leading to high false positive rates and missed detections. Machine learning techniques offer a promising solution by leveraging historical data to identify fraudulent patterns. This article explores using the personalised PageRank (PPR) algorithm to capture the social dynamics of fraud by analysing relationships between financial accounts. The primary objective is to compare the performance of traditional features with the addition of PPR in fraud detection models. Results indicate that integrating PPR enhances the model's predictive power, surpassing the baseline model. Additionally, the PPR feature provides unique and valuable information, evidenced by its high feature importance score. Feature stability analysis confirms consistent feature distributions across training and test datasets. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.09495&r=net |