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on Network Economics |
| By: | Claudia Pigini; Alessandro Pionati; Francesco Valentini |
| Abstract: | We study the application of the Grouped Fixed Effects (GFE) estimator (Bonhomme et al., ECMTA 90(2):625-643, 2022) to binary choice models for network and panel data. This approach discretizes unobserved heterogeneity via k-means clustering and performs maximum likelihood estimation, reducing the number of fixed effects in finite samples. This regularization helps analyze small/sparse networks and rare events by mitigating complete separation, which can lead to data loss. We focus on dynamic models with few state transitions and network formation models for sparse networks. The effectiveness of this method is demonstrated through simulations and real data applications. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.06446 |
| By: | Yuya Sasaki |
| Abstract: | I present a novel uniform law of large numbers (ULLN) for network-dependent data. While Kojevnikov, Marmer, and Song (KMS, 2021) provide a comprehensive suite of limit theorems and a robust variance estimator for network-dependent processes, their analysis focuses on pointwise convergence. On the other hand, uniform convergence is essential for nonlinear estimators such as M and GMM estimators (e.g., Newey and McFadden, 1994, Section 2). Building on KMS, I establish the ULLN under network dependence and demonstrate its utility by proving the consistency of both M and GMM estimators. A byproduct of this work is a novel maximal inequality for network data, which may prove useful for future research beyond the scope of this paper. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.00290 |
| By: | Jan Fialkowski; Christian Diem; Andr\'as Borsos; Stefan Thurner |
| Abstract: | Supply chain disruptions constitute an often underestimated risk for financial stability. As in financial networks, systemic risks in production networks arises when the local failure of one firm impacts the production of others and might trigger cascading disruptions that affect significant parts of the economy. Here, we study how systemic risk in production networks translates into financial systemic risk through a mechanism where supply chain contagion leads to correlated bank-firm loan defaults. We propose a financial stress-testing framework for micro- and macro-prudential applications that features a national firm level supply chain network in combination with interbank network layers. The model is calibrated by using a unique data set including about 1 million firm-level supply links, practically all bank-firm loans, and all interbank loans in a small European economy. As a showcase we implement a real COVID-19 shock scenario on the firm level. This model allows us to study how the disruption dynamics in the real economy can lead to interbank solvency contagion dynamics. We estimate to what extent this amplifies financial systemic risk. We discuss the relative importance of these contagion channels and find an increase of interbank contagion by 70% when production network contagion is present. We then examine the financial systemic risk firms bring to banks and find an increase of up to 28% in the presence of the interbank contagion channel. This framework is the first financial systemic risk model to take agent-level dynamics of the production network and shocks of the real economy into account which opens a path for directly, and event-driven understanding of the dynamical interaction between the real economy and financial systems. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.17044 |
| By: | Evsyukova, Yulia; Rusche, Felix; Mill, Wladislaw |
| 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: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:312188 |
| By: | Pawanesh Pawanesh; Charu Sharma; Niteesh Sahni |
| Abstract: | In financial networks, information does not always follow the shortest path between two nodes but may also take alternate routes. Communicability, a network measure, resolves this complexity and, in diffusion-like processes, provides a reliable measure of the ease with which information flows between nodes. As a result, communicability appears to be an important measure for detecting disturbances in connectivity within financial systems, similar to instability caused by periods of high volatility. This study investigates the evolution of communicability measures in the stock networks during periods of crises, showing how systemic shocks strengthen the pairwise interdependence between stocks in the financial market. In this study, the permutation test reveals that approximately 83.5 per cent of stock pairs were found to be statistically significant at the significance level of 0.001 and have an increase in the shortest communicability path length during the crisis than the normal days, indicating enhanced interdependence and heightened information flow in the market. Furthermore, we show that when employed as features in the classification model, the network shortest path-based measures, along with communicability measures, are able to accurately classify between the times periods of market stability and volatility. Additionally, our results show that the geometric measures perform better in terms of classification accuracy than topological measures. These findings provide important insights into market behaviour during times of increased volatility and advance our understanding of the financial market crisis. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.08242 |
| By: | Kotkaniemi, Anniina; Ylä-Anttila, Tuomas; Chen, Ted Hsuan Yun |
| Abstract: | Social media is an important arena of contestation for policy actors. Yet, little research has explored the relationship between policy actors’ behaviour online and offline. In this study, we focus on actor influence, a key aspect of policy systems, by exploring four types of policy influence. We ask 1) are actors influential in policy-making central in social media networks? and 2) are they able to shape the structure of policy communication on social media? Using exponential random graph models on survey and Twitter data from the Finnish climate policy domain, we find that reputationally influential actors in offline policy-making are also central online, but the pattern does not hold for those with offline formal-institutional influence. Further, offline influence does not translate to being an online influencer; actors influential offline do not shape the structure of the Twitter network. Our results suggest that online influence is partially distinct from influence offline. |
| Date: | 2023–07–05 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:dnrg6_v1 |
| By: | Herrera-Guzmán, Yessica (Northeastern University); Gates, Alexander; Candia, Cristian; Barabasi, Albert Laszlo |
| Abstract: | In the recent decade, we have seen major progress in quantifying the behaviors and the impact of scientists, resulting in a quantitative toolset capable of monitoring and predicting the career patterns of the profession. It is unclear, however, if this toolset applies to other creative domains beyond the sciences. In particular, while performance in the arts has long been difficult to quantify objectively, research suggests that professional networks and prestige of affiliations play a similar role to those observed in science, hence they can reveal patterns underlying successful careers. To test this hypothesis, here we focus on ballet, as it allows us to investigate in a quantitative fashion the interplay of individual performance, institutional prestige, and network effects. We analyze data on competition outcomes from 6, 363 ballet students affiliated with 1, 603 schools in the United States, who participated in the Youth America Grand Prix (YAGP) between 2000 and 2021. Through multiple logit models and matching experiments, we provide evidence that schools' strategic network position bridging between communities captures social prestige and predicts the placement of students into jobs in ballet companies. This work reveals the importance of institutional prestige on career success in ballet and showcases the potential of network science approaches to provide quantitative viewpoints for the professional development of careers beyond science. |
| Date: | 2023–06–17 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:x9zwn_v1 |
| By: | Shiqi Wang; Zhibo Zhang; Libing Fang; Cam-Tu Nguyen; Wenzhon Li |
| Abstract: | Corporate fraud detection aims to automatically recognize companies that conduct wrongful activities such as fraudulent financial statements or illegal insider trading. Previous learning-based methods fail to effectively integrate rich interactions in the company network. To close this gap, we collect 18-year financial records in China to form three graph datasets with fraud labels. We analyze the characteristics of the financial graphs, highlighting two pronounced issues: (1) information overload: the dominance of (noisy) non-company nodes over company nodes hinders the message-passing process in Graph Convolution Networks (GCN); and (2) hidden fraud: there exists a large percentage of possible undetected violations in the collected data. The hidden fraud problem will introduce noisy labels in the training dataset and compromise fraud detection results. To handle such challenges, we propose a novel graph-based method, namely, Knowledge-enhanced GCN with Robust Two-stage Learning (${\rm KeGCN}_{R}$), which leverages Knowledge Graph Embeddings to mitigate the information overload and effectively learns rich representations. The proposed model adopts a two-stage learning method to enhance robustness against hidden frauds. Extensive experimental results not only confirm the importance of interactions but also show the superiority of ${\rm KeGCN}_{R}$ over a number of strong baselines in terms of fraud detection effectiveness and robustness. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.19305 |
| By: | Demir, Gökay; Hertweck, Friederike; Sandner, Malte; Yükselen, Ipek |
| Abstract: | This paper analyzes the impact of college students' coworker networks formed during student jobs on their labor market outcomes after graduation. For our analysis, we use novel data that links students' administrative university records with their pre- and post-graduation employment registry data and their coworker networks. Our empirical strategy exploits variation in the timing and duration of student jobs, controlling for a variety of individual and network characteristics, as well as firm-by-occupation fixed effects, eliminating potential selection bias arising from non-random entry into student jobs and networks. The results show that students who work alongside higher-earning coworkers during their student jobs earn higher wages in their first post-graduation employment. Two key mechanisms appear to drive this effect: (1) sorting into higher-paying firms after graduation, facilitated by coworker referrals, and (2) enhanced field-specific human capital through exposure to skilled colleagues. However, the initial wage advantage from higher-earning coworker networks diminishes over time as students with worse networks catch up. Our findings contribute to the understanding of how early career networks shape labor market outcomes and facilitate a smoother transition from higher education to graduate employment. |
| Abstract: | In diesem Beitrag wird untersucht, wie sich die während Studentenjobs gebildeten Netzwerke auf den Arbeitsmarkteintritt von Studierenden nach Abschluss des Studiums auswirken. Für unsere Analyse verwenden wir eine neuartige Datenverknüpfung aus administrativen Universitätsdaten von Studierenden und Sozialversicherungsdaten der Studierenden sowie all ihrer Kolleginnen und Kollegen während der Studentenjobs. Unsere empirische Strategie nutzt Variationen im Zeitpunkt und in der Dauer der Studentenjobs und kontrolliert für eine Vielzahl von individuellen und Netzwerk-Charakteristika sowie für fixe Effekte zu Unternehmen und der Berufsgruppe, um mögliche Verzerrungen zu eliminieren, die sich aus der nicht zufälligen Selektion der Studierenden in bestimmte Studentenjobs und Netzwerke ergeben. Die Ergebnisse zeigen, dass Studierende, die während ihrer Studentenjobs mit besser verdienenden Kollegen zusammenarbeiten, in ihrer ersten Anstellung nach dem Studium höhere Löhne erzielen. Zwei Mechanismen scheinen für diesen Effekt verantwortlich zu sein: (1) Sortierung der Studierenden in besser zahlende Unternehmen nach dem Abschluss (unterstützt durch Empfehlungen ehemaliger Kolleginnen und Kollegen) und (2) verbessertes fachspezifisches Humankapital durch den Kontakt mit qualifizierten Kolleginnen und Kollegen während des Studentenjobs. Der anfängliche Lohnvorteil durch die besser verdienenden Netzwerke nimmt jedoch im Laufe der Zeit ab, da Studierende mit schlechteren Netzwerken aufholen. |
| Keywords: | Coworker networks, student jobs, labor market entry, wages |
| JEL: | I23 J24 J31 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:rwirep:311298 |
| By: | González-Casado, Miguel A.; Gonzales, Gladis; Molina, Jose Luis; Sánchez, Angel |
| Abstract: | In this study, we present a method to uncover the fundamental dimensions of structural variability in Personal Networks (PNs) and develop a classification solely based on these structural properties. We address the limitations of previous literature and lay the foundation for a rigorous methodology to construct a Structural Typology of PNs. We test our method with a dataset of nearly 8, 000 PNs belonging to high school students. We find that the structural variability of these PNs can be described in terms of six basic dimensions encompassing community and cohesive subgroup structure, as well as levels of cohesion, hierarchy, and centralization. Our method allows us to categorize these PNs into eight types and to interpret them structurally. We assess the robustness and generality of our methodology by comparing with previous results on structural typologies. To encourage its adoption, its improvement by others, and to support future research, we provide a publicly available Python class, enabling researchers to utilize our method and test the universality of our results. |
| Date: | 2023–10–10 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:23efd_v1 |
| By: | Kiran Tomlinson; Johan Ugander; Jon Kleinberg |
| Abstract: | Recent research on instant runoff voting (IRV) shows that it exhibits a striking combinatorial property in one-dimensional preference spaces: there is an "exclusion zone" around the median voter such that if a candidate from the exclusion zone is on the ballot, then the winner must come from the exclusion zone. Thus, in one dimension, IRV cannot elect an extreme candidate as long as a sufficiently moderate candidate is running. In this work, we examine the mathematical structure of exclusion zones as a broad phenomenon in more general preference spaces. We prove that with voters uniformly distributed over any $d$-dimensional hyperrectangle (for $d > 1$), IRV has no nontrivial exclusion zone. However, we also show that IRV exclusion zones are not solely a one-dimensional phenomenon. For irregular higher-dimensional preference spaces with fewer symmetries than hyperrectangles, IRV can exhibit nontrivial exclusion zones. As a further exploration, we study IRV exclusion zones in graph voting, where nodes represent voters who prefer candidates closer to them in the graph. Here, we show that IRV exclusion zones present a surprising computational challenge: even checking whether a given set of positions is an IRV exclusion zone is NP-hard. We develop an efficient randomized approximation algorithm for checking and finding exclusion zones. We also report on computational experiments with exclusion zones in two directions: (i) applying our approximation algorithm to a collection of real-world school friendship networks, we find that about 60% of these networks have probable nontrivial IRV exclusion zones; and (ii) performing an exhaustive computer search of small graphs and trees, we also find nontrivial IRV exclusion zones in most graphs. While our focus is on IRV, the properties of exclusion zones we establish provide a novel method for analyzing voting systems in metric spaces more generally. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.16719 |
| By: | Eric Hitz; Mingmin Feng; Radu Tanase; Ren\'e Algesheimer; Manuel S. Mariani |
| Abstract: | Recent advances in artificial intelligence have led to the proliferation of artificial agents in social contexts, ranging from education to online social media and financial markets, among many others. The increasing rate at which artificial and human agents interact makes it urgent to understand the consequences of human-machine interactions for the propagation of new ideas, products, and behaviors in society. Across two distinct empirical contexts, we find here that artificial agents lead to significantly faster and wider social contagion. To this end, we replicate a choice experiment previously conducted with human subjects by using artificial agents powered by large language models (LLMs). We use the experiment's results to measure the adoption thresholds of artificial agents and their impact on the spread of social contagion. We find that artificial agents tend to exhibit lower adoption thresholds than humans, which leads to wider network-based social contagions. Our findings suggest that the increased presence of artificial agents in real-world networks may accelerate behavioral shifts, potentially in unforeseen ways. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.21037 |
| By: | Honglin Fu; Yebo Feng; Cong Wu; Jiahua Xu |
| Abstract: | Masterminds are entities organizing, coordinating, and orchestrating cryptocurrency pump-and-dump schemes, a form of trade-based manipulation undermining market integrity and causing financial losses for unwitting investors. Previous research detects pump-and-dump activities in the market, predicts the target cryptocurrency, and examines investors and \ac{osn} entities. However, these solutions do not address the root cause of the problem. There is a critical gap in identifying and tracing the masterminds involved in these schemes. In this research, we develop a detection system \textsc{Perseus}, which collects real-time data from the \acs{osn} and cryptocurrency markets. \textsc{Perseus} then constructs temporal attributed graphs that preserve the direction of information diffusion and the structure of the community while leveraging \ac{gnn} to identify the masterminds behind pump-and-dump activities. Our design of \textsc{Perseus} leads to higher F1 scores and precision than the \ac{sota} fraud detection method, achieving fast training and inferring speeds. Deployed in the real world from February 16 to October 9 2024, \textsc{Perseus} successfully detects $438$ masterminds who are efficient in the pump-and-dump information diffusion networks. \textsc{Perseus} provides regulators with an explanation of the risks of masterminds and oversight capabilities to mitigate the pump-and-dump schemes of cryptocurrency. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.01686 |
| By: | Michael Bailey; Drew M. Johnston; Theresa Kuchler; Ayush Kumar; Johannes Stroebel |
| Abstract: | We introduce, analyze, and describe subnational data on cross-gender friendships for nearly 200 countries and territories, using data from 1.38 trillion ties between 1.8 billion Facebook users. Homophily by gender exists nearly everywhere, with individuals' strongest ties exhibiting less homophily than their peripheral connections. Across countries, cross-gender friendship rates align with existing measures of gender disparities. Within countries, cross-gender friending rates correlate with support for gender equality. In the US, cross-gender friendships are rarer in areas with a larger White share of the population, higher incomes, and more per-capita religious congregations. |
| JEL: | H0 R0 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33480 |
| By: | Shengyuan Huang; Wenjun Mei; Xiaoguang Yang; Zhigang Cao |
| Abstract: | This paper studies allocation mechanisms in max-flow games with players' capacities as private information. We first show that no core-selection mechanism is truthful: there may exist a player whose payoff increases if she under-reports her capacity when a core-section mechanism is adopted. We then introduce five desirable properties for mechanisms in max-flow games: DSIC (truthful reporting is a dominant strategy), SIR (individual rationality and positive payoff for each player contributing positively to at least one coalition), SP (no edge has an incentive to split into parallel edges), MP (no parallel edges have incentives to merge), and CM (a player's payoff does not decrease as another player's capacity and max-flow increase). While the Shapley value mechanism satisfies DSIC and SIR, it fails to meet SP, MP and CM. We propose a new mechanism based on minimal cuts that satisfies all five properties. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.08248 |