nep-net New Economics Papers
on Network Economics
Issue of 2023‒05‒08
ten papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Towards social network metrics for supply network circularity By Leonardo Marques; Marina Dastre Manzanares
  2. Homophily and Transmission of Behavioral Traits in Social Networks By Palaash Bhargava; Daniel L. Chen; Matthias Sutter; Camille Terrier
  3. A Theory of Payments-Chain Crises By Saki Bigio
  4. Physicians Treating Physicians: Relational and Informational Advantages in Treatment and Survival By Chen, Stacey H.; Chen, Jennjou; Chuang, Hongwei; Lin, Tzu-Hsin
  5. The role of the structure of social relations in achieving academic success By Cherenkova Kseniya; Mirzoyan Ashot
  6. Trade Networks and Natural Disasters: Diversion, not Destruction By Gigout, Timothee; London, Melina
  7. Mapping job complexity and skills into wages By Sabrina Aufiero; Giordano De Marzo; Angelica Sbardella; Andrea Zaccaria
  8. The Value of Information and Circular Settings By Stefan Behringer; Roman V. Bellavkin
  9. Asymmetric networks, clientelism and their impacts: households' access to workfare employment in rural India By Anindya Bhattacharya; Anirban Kar; Alita Nandi
  10. OFTER: An Online Pipeline for Time Series Forecasting By Nikolas Michael; Mihai Cucuringu; Sam Howison

  1. By: Leonardo Marques (Audencia Business School); Marina Dastre Manzanares (UFRJ - Universidade Federal do Rio de Janeiro)
    Abstract: Purpose Despite the systemic nature of circular economy (CE), theorisation that draws from a supply network perspective is only incipient. Moreover, the operations and supply chain management (OSCM) field has engaged in little dialogue with circularity. This study explores social network analysis (SNA) to depict how the shift from linear to circular not only leads to higher rates of resource economy, repair and recycle but also reshapes governance dynamics and network structure of supply networks. Design/methodology/approach The study departs from a systematic review of the literature and draws from core concepts in OSCM, CE and SNA to offer theoretical propositions that articulate how social network metrics can depict supply network circularity. The framework is illustrated with examples from fashion and electronics industries. Findings Four theoretical propositions enlighten how betweenness centrality, eigenvector centrality and network density can explain the shift from linear to circular supply networks across the three CE strategies of narrowing, slowing and closing. Originality/value The combination of biomimicry, CE, the push–pull dichotomy and social network metrics offer a theory-driven framework for supply network circularity.
    Keywords: Circularity, Circular economy, Supply network, Governance, Social network analysis, Theory development, Circular economy supply network governance social network analysis theory development, supply network, governance, social network analysis, theory development
    Date: 2022–08–18
  2. By: Palaash Bhargava; Daniel L. Chen; Matthias Sutter; Camille Terrier
    Abstract: Social networks are a key factor of success in life, but they are also strongly segmented on gender, ethnicity, and other demographic characteristics (Jackson, 2010). We present novel evidence on an understudied source of homophily: behavioral traits. Behavioral traits are important determinants of life outcomes. While recent work has focused on how these traits are influenced by the family environment, or how they can be affected by childhood interventions, little is known about how these traits are related to social networks. Based on unique data collected using incentivized experiments on more than 2, 500 French high-school students, we find high levels of homophily across all ten behavioral traits that we study. Notably, the extent of homophily depends on similarities in demographic characteristics, in particular with respect to gender. Furthermore, the larger the number of behavioral traits that students share, the higher the overall homophily. Using network econometrics, we show that the observed homophily is not only an outcome of endogenous network formation, but is also a result of friends influencing each others’ behavioral traits. Importantly, the transmission of traits is larger when students share demographic characteristics, such as gender, have longer periods of friendship, or are friends with more popular individuals.
    Keywords: homophily, social networks, behavioural traits, peer effects, experiments
    JEL: D85 C91 D01 D90
    Date: 2023
  3. By: Saki Bigio (UCLA; NBER)
    Abstract: This paper introduces an endogenous network of payments chains into a business cycle model. Agents order production in bilateral relations. Some payments are executed immediately. Other payments, chained payments, are delayed until other payments are executed. Because production starts only after orders are paid, chained payments induce production delays. In equilibrium, agents choose the amount of chained payments given interest rates and access to internal funds or credit lines. This choice determines the payments-chain network and aggregate total-factor productivity (TFP). The paper characterizes equilibrium dynamics and their innate inefficiencies. Agents internalize the direct costs of their payment delays, but do not internalize the costs induced onto others. This externality produces novel policy insights and rationalizes permanent reductions in TFP under excessive debt.
    Keywords: Payments, Networks, Business Cycles
    Date: 2023–04
  4. By: Chen, Stacey H. (University of Tokyo); Chen, Jennjou (National Chengchi University); Chuang, Hongwei (International University of Japan); Lin, Tzu-Hsin (National Taiwan University)
    Abstract: We use the medical specialties of physician-patients with advanced cancer to study the role of knowledge versus networks in treatment choices and patient survival by matching comparable patients with doctors and admission periods to control unobserved doctor quality. Physician-patients are less likely to have surgery, radiation, or checkups and more likely to receive targeted therapy, spend more on drugs, enjoy a higher survival rate, and spend less on coinsurance than non-physician-patients. Knowledge mechanisms play a crucial role because the network effect explains some, but not all, patterns. For less informed physician-patients, possessing a network is equivalent to reducing medical knowledge.
    Keywords: physician quality, social ties, communication, information
    JEL: D83 I11 J44
    Date: 2023–03
  5. By: Cherenkova Kseniya (Department of Economics, Lomonosov Moscow State University); Mirzoyan Ashot (Department of Economics, Lomonosov Moscow State University)
    Abstract: This paper assesses the impact of the network of social interactions on the academic results of students who studied at the Faculty of Economics of Lomonosov Moscow State University in 2021-2022 academic year in the third year of study. The study examines the impact of social interaction on compulsory math subjects of 2-3 courses: Mathematical statistics and Econometrics. Data on the structure of the social network was collected using a survey, which allows you to accurately determine which students influence each other. Effect effect social interaction was assessed using a linear-averaged model. To eliminate the effect of self-selection, a tool is formed with which the presence of the influence of "friends through one handshake", which is exogenous, is checked. The stability of the results is checked by forming a fake network with a random structure of connections. In the work, it was possible to identify the effect of the influence of social interactions on the results of the econometrics exam: on average, all other things being equal, the better the student's friends study, the better he studies by himself. At the same time, there was no significant effect of social ties on the results of the exam in Mathematical Statistics: this can be explained by the fact that students took the exam remotely. Additional research is required to test this hypothesis .
    Keywords: Social connections, students, academic success, neighbor effect, network effects
    JEL: A22 A29
    Date: 2022–12
  6. By: Gigout, Timothee (Banque de France); London, Melina (European Commission)
    Abstract: We study how international trade networks react to natural disasters. We combine exhaustive firm-to-firm trade credit and disaster data and use a dynamic difference-in-differences identification strategy. We establish the causal effect of natural disasters abroad on the size, shape and quality of French exporters' international trade networks. We find evidence of large and persistent disruptions to international buyer-supplier relationships. This leads to a restructuring of the trade network of the largest French exporters and a change in trade finance sources for affected countries. We find strong and permanent negative effects on the trade credit sales of French suppliers to affected destinations. The largest firms are driving the response, both on the supplier and buyer side. Trade network restructuring towards unaffected destinations is higher for large multinationals trading more homogeneous products. This effect operates exclusively through a reduction in the number of buyers. This induces a negative shift in the distribution of the quality of buyers in the destination affected by the natural disaster.
    Keywords: Firm Dynamics; Trade Networks; Natural Disaster, Granularity
    JEL: E32 F14 F23 F44 L14
    Date: 2023–02
  7. By: Sabrina Aufiero; Giordano De Marzo; Angelica Sbardella; Andrea Zaccaria
    Abstract: We use algorithmic and network-based tools to build and analyze the bipartite network connecting jobs with the skills they require. We quantify and represent the relatedness between jobs and skills by using statistically validated networks. Using the fitness and complexity algorithm, we compute a skill-based complexity of jobs. This quantity is positively correlated with the average salary, abstraction, and non-routinarity level of jobs. Furthermore, coherent jobs - defined as the ones requiring closely related skills - have, on average, lower wages. We find that salaries may not always reflect the intrinsic value of a job, but rather other wage-setting dynamics that may not be directly related to its skill composition. Our results provide valuable information for policymakers, employers, and individuals to better understand the dynamics of the labor market and make informed decisions about their careers.
    Date: 2023–04
  8. By: Stefan Behringer (Department of Economics, University of Bielefeld, Sciences Po); Roman V. Bellavkin (School of Sciences and Technology, Middlesex University of London)
    Abstract: We present a universal concept for the Value of Information (VoI) based on Claude Shannon's information and work of Ruslan Stratonovich that has desirable properties for Bayesian decision theory and demand analysis. The Shannon/Stratonovich VoI concept is compared to the concept of Hartley VoI and applied to an epitome economic application of a circular setting generalizing an example of Ruslan Stratonovich and allowing for a network structure and an investigation of various economic transport costs.
    Date: 2023–03
  9. By: Anindya Bhattacharya; Anirban Kar; Alita Nandi
    Abstract: In this paper we explore two intertwined issues. First, using primary data we examine the impact of asymmetric networks, built on rich relational information on several spheres of living, on access to workfare employment in rural India. We find that unidirectional relations, as opposed to reciprocal relations, and the concentration of such unidirectional relations increase access to workfare jobs. Further in-depth exploration provides evidence that patron-client relations are responsible for this differential access to such employment for rural households. Complementary to our empirical exercises, we construct and analyse a game-theoretical model supporting our findings.
    Date: 2023–04
  10. By: Nikolas Michael; Mihai Cucuringu; Sam Howison
    Abstract: We introduce OFTER, a time series forecasting pipeline tailored for mid-sized multivariate time series. OFTER utilizes the non-parametric models of k-nearest neighbors and Generalized Regression Neural Networks, integrated with a dimensionality reduction component. To circumvent the curse of dimensionality, we employ a weighted norm based on a modified version of the maximal correlation coefficient. The pipeline we introduce is specifically designed for online tasks, has an interpretable output, and is able to outperform several state-of-the art baselines. The computational efficacy of the algorithm, its online nature, and its ability to operate in low signal-to-noise regimes, render OFTER an ideal approach for financial multivariate time series problems, such as daily equity forecasting. Our work demonstrates that while deep learning models hold significant promise for time series forecasting, traditional methods carefully integrating mainstream tools remain very competitive alternatives with the added benefits of scalability and interpretability.
    Date: 2023–04

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