nep-net New Economics Papers
on Network Economics
Issue of 2024‒03‒11
nine papers chosen by
Alfonso Rosa García, Universidad de Murcia


  1. The role of friends in the opioid epidemic By Adamopoulou, Effrosyni; Greenwood, Jeremy; Guner, Nezih; Kopecky, Karen A.
  2. CAPACITY OF LOGISTICS NETWORKS TO EVOLVE IN TIMES OF CRISIS By Nicolas Jouve; Ludovic Vaillant; Corinne Blanquart
  3. Empirical Estimation of the Propagation of Investment Spikes over the Production Network By NIREI Makoto
  4. Global bank network connectedness revisited: What is common, idiosyncratic and when? By Jonas Krampe; Luca Margaritella
  5. Cultural Ties in Knowledge Production By Yan, Xiaoqin; Bao, Honglin; Leppard, Tom; Davis, Andrew
  6. Maximizing NFT Incentives: References Make You Rich By Guangsheng Yu; Qin Wang; Caijun Sun; Lam Duc Nguyen; H. M. N. Dilum Bandara; Shiping Chen
  7. Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity By Yike Wang; Chris Gu; Taisuke Otsu
  8. Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk By Katerina Rigana; Ernst C. Wit; Samantha Cook
  9. Rules of attraction: Networks of innovation policy makers in the EU By Laatsit, Mart; Boschma, Ron

  1. By: Adamopoulou, Effrosyni; Greenwood, Jeremy; Guner, Nezih; Kopecky, Karen A.
    Abstract: The role of friends in the US opioid epidemic is examined. Using data from the National Longitudinal Survey of Adolescent Health (Add Health), adults aged 25-34 and their high school best friends are focused on. An instrumental variable technique is employed to estimate peer effects in opioid misuse. Severe injuries in the previous year are used as an instrument for opioid misuse in order to estimate the causal impact of someone misusing opioids on the probability that their best friends also misuse. The estimated peer effects are significant: Having a best friend with a reported serious injury in the previous year increases the probability of own opioid misuse by around 7 percentage points in a population where 17 percent ever misuses opioids. The effect is driven by individuals without a college degree and those who live in the same county as their best friends.
    Keywords: opioid, peer-group effects, friends, instrumental variables, Add Health, severe injuries
    JEL: C26 D10 I12 J11
    Date: 2024
    URL: http://d.repec.org/n?u=RePEc:zbw:zewdip:283015&r=net
  2. By: Nicolas Jouve (MATRiS - Mobilité, Aménagement, Transports, Risques et Société - Cerema - Centre d'Etudes et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement - CY - CY Cergy Paris Université); Ludovic Vaillant (MATRiS - Mobilité, Aménagement, Transports, Risques et Société - Cerema - Centre d'Etudes et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement - CY - CY Cergy Paris Université); Corinne Blanquart
    Abstract: The COVID-19 crisis, unprecedented in terms of its global nature and duration, highlighted the weak points of logistics networks, bringing to the forefront the recommendations for reducing the vulnerability of these networks. The objective of this article is to question the adaptability of logistics organisations, their capacity to evolve towards new ways of doing things in order to cope with the uncertainty inherent in crises. The article is based on the work of Burmeister (2000) who classifies logistics and transportation organizations into ‘logistics families'. In each of the four families, coordination between professionals is structured by a framework value, a reference shared by the actors of the family considered to make their choices. The question raised is therefore whether the COVID-19 crisis has led to a shift in certain relationships between professionals from one family to another, in other words, a shift in framework values. The survey on which this research is based shows that, although the actors of a given logistics family adapt their logistics and transport strategies to crisis situations, these strategies are always based on the same framework value. Logistics families are therefore crisisresistant. Thus, the relations between these actors, the choices they make, remain guided by the framework values that governed their relations before the crisis. The methods of coordination between the actors in logistics networks do not change during a crisis. This permanence of the coordination structure is not, however, a limit to the profound change in logistics networks, which remain adaptable.
    Keywords: supply chains, coordination, adaptability, crisis
    Date: 2023–06–15
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-04432442&r=net
  3. By: NIREI Makoto
    Abstract: This study estimates the degree of complementarity between firm investment spikes linked by production networks. A customer firm’s increase in capital by more than 20% (an investment spike) raises its future demand for intermediate inputs, increasing the likelihood of the supplier’s spike. Similarly, a supplier’s investment spike lowers the future cost of intermediate goods demanded by its customer and induces the customer’s investment spike. We use firm-level panel data from the Japanese business survey and transaction network data to estimate this complementarity in investment decisions. The estimates show that one firm’s investment spike induces, on average, 0.088 firms to conduct investment spikes, indicating that an investment spike shock can propagate through the production network to upstream and downstream firms.
    Date: 2024–02
    URL: http://d.repec.org/n?u=RePEc:eti:dpaper:24029&r=net
  4. By: Jonas Krampe; Luca Margaritella
    Abstract: We revisit the problem of estimating high-dimensional global bank network connectedness. Instead of directly regularizing the high-dimensional vector of realized volatilities as in Demirer et al. (2018), we estimate a dynamic factor model with sparse VAR idiosyncratic components. This allows to disentangle: (I) the part of system-wide connectedness (SWC) due to the common component shocks (what we call the "banking market"), and (II) the part due to the idiosyncratic shocks (the single banks). We employ both the original dataset as in Demirer et al. (2018) (daily data, 2003-2013), as well as a more recent vintage (2014-2023). For both, we compute SWC due to (I), (II), (I+II) and provide bootstrap confidence bands. In accordance with the literature, we find SWC to spike during global crises. However, our method minimizes the risk of SWC underestimation in high-dimensional datasets where episodes of systemic risk can be both pervasive and idiosyncratic. In fact, we are able to disentangle how in normal times $\approx$60-80% of SWC is due to idiosyncratic variation and only $\approx$20-40% to market variation. However, in crises periods such as the 2008 financial crisis and the Covid19 outbreak in 2019, the situation is completely reversed: SWC is comparatively more driven by a market dynamic and less by an idiosyncratic one.
    Date: 2024–02
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2402.02482&r=net
  5. By: Yan, Xiaoqin; Bao, Honglin; Leppard, Tom; Davis, Andrew
    Abstract: This paper investigates the cultural ties in American sociology defined by the shared usage of cultural symbols across schools. Cultural symbols are operationalized as research focuses from the dissertations of a school’s graduates. We construct a unique pairwise dataset including 6, 441 school pairs across 114 schools, detailing their dyadic relationships (e.g., geographical co-residence) and cultural proximity inferred from dissertations. We build a socio-cultural network where a school sends a tie to another when their proximity is sufficiently high. We design information theory-driven computational linguistic methods to identify gatekeeping symbols co-used by reciprocally connected schools within the same cultural niche, differentiating them from other schools in the network. Our findings reveal two major school clusters and their research trajectories, with one representing dominant trends in relatively esoteric areas like sociology of culture, economic life, organizations, and politics and the other a more explicit focus on social problems. Employing dyadic-cluster-robust inference with school-fixed effects, we discern key determinants that shape cultural convergence and distinction, including school prestige, geographical co-residence, and institutional classification. In sum, our study proposes a pipeline for measuring the strengths and symbols of cultural ties across schools and understanding the factors that influence the development of duality between schools and schools of thought.
    Date: 2024–02–03
    URL: http://d.repec.org/n?u=RePEc:osf:socarx:qvyj8&r=net
  6. By: Guangsheng Yu; Qin Wang; Caijun Sun; Lam Duc Nguyen; H. M. N. Dilum Bandara; Shiping Chen
    Abstract: In this paper, we study how to optimize existing Non-Fungible Token (NFT) incentives. Upon exploring a large number of NFT-related standards and real-world projects, we come across an unexpected finding. That is, the current NFT incentive mechanisms, often organized in an isolated and one-time-use fashion, tend to overlook their potential for scalable organizational structures. We propose, analyze, and implement a novel reference incentive model, which is inherently structured as a Directed Acyclic Graph (DAG)-based NFT network. This model aims to maximize connections (or references) between NFTs, enabling each isolated NFT to expand its network and accumulate rewards derived from subsequent or subscribed ones. We conduct both theoretical and practical analyses of the model, demonstrating its optimal utility.
    Date: 2024–02
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2402.06459&r=net
  7. By: Yike Wang; Chris Gu; Taisuke Otsu
    Abstract: This paper presents a novel application of graph neural networks for modeling and estimating network heterogeneity. Network heterogeneity is characterized by variations in unit's decisions or outcomes that depend not only on its own attributes but also on the conditions of its surrounding neighborhood. We delineate the convergence rate of the graph neural networks estimator, as well as its applicability in semiparametric causal inference with heterogeneous treatment effects. The finite-sample performance of our estimator is evaluated through Monte Carlo simulations. In an empirical setting related to microfinance program participation, we apply the new estimator to examine the average treatment effects and outcomes of counterfactual policies, and to propose an enhanced strategy for selecting the initial recipients of program information in social networks.
    Date: 2024–01
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2401.16275&r=net
  8. By: Katerina Rigana; Ernst C. Wit; Samantha Cook
    Abstract: Accurately defining, measuring and mitigating risk is a cornerstone of financial risk management, especially in the presence of financial contagion. Traditional correlation-based risk assessment methods often struggle under volatile market conditions, particularly in the face of external shocks, highlighting the need for a more robust and invariant predictive approach. This paper introduces the Causal Network Contagion Value at Risk (Causal-NECO VaR), a novel methodology that significantly advances causal inference in financial risk analysis. Embracing a causal network framework, this method adeptly captures and analyses volatility and spillover effects, effectively setting it apart from conventional contagion-based VaR models. Causal-NECO VaR's key innovation lies in its ability to derive directional influences among assets from observational data, thereby offering robust risk predictions that remain invariant to market shocks and systemic changes. A comprehensive simulation study and the application to the Forex market show the robustness of the method. Causal-NECO VaR not only demonstrates predictive accuracy, but also maintains its reliability in unstable financial environments, offering clearer risk assessments even amidst unforeseen market disturbances. This research makes a significant contribution to the field of risk management and financial stability, presenting a causal approach to the computation of VaR. It emphasises the model's superior resilience and invariant predictive power, essential for navigating the complexities of today's ever-evolving financial markets.
    Date: 2024–02
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2402.06032&r=net
  9. By: Laatsit, Mart (CIRCLE, Lund University); Boschma, Ron (Utrecht University)
    Abstract: Policy networks are an important source of information for policy making. Yet, we have only a limited understanding of how policy networks are structured among innovation policy makers and which factors shape their structure. This paper studies how proximities can explain what drives the connections in policy networks. More specifically, we look at innovation policy networks between EU member states. We use social network analysis based on our own data to map the networks of the 28 EU innovation policy directors, consisting of 756 potential connections, and study the proximities shaping these networks. Geographical and cultural proximity turn out to be strong predictors for symmetric and asymmetric ties, but we do not find a relationship between policy proximity (in terms of similarities in business environment regulations and innovation policy) and policy network formation between countries.
    Keywords: Innovation policy; policy networks; proximities; policy proximity; social network analysis
    JEL: O33 O38
    Date: 2024–02–14
    URL: http://d.repec.org/n?u=RePEc:hhs:lucirc:2024_003&r=net

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