nep-net New Economics Papers
on Network Economics
Issue of 2021‒11‒15
six papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Clubs and Networks By Ding, S.; Dziubinski, M.; Goyal, S.
  2. Evaluating structural edge importance in temporal networks By Seabrook, Isobel E.; Barucca, Paolo; Caccioli, Fabio
  3. Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States By Pongou, Roland; Tchuente, Guy; Tondji, Jean-Baptiste
  4. Crisis Propagation in a Heterogeneous Self-Reflexive DSGE Model By Federico Guglielmo Morelli; Michael Benzaquen; Jean-Philippe Bouchaud; Marco Tarzia
  5. Can you always reap what you sow? Network and functional data analysis of VC investments in health-tech companies By Christian Esposito; Marco Gortan; Lorenzo Testa; Francesca Chiaromonte; Giorgio Fagiolo; Andrea Mina; Giulio Rossetti
  6. Avian influenza transmission risk along live poultry trading networks in Bangladesh By Moyen, Natalie; Hoque, Md Ahasanul; Mahmud, Rashed; Hasan, Mahmudul; Sarkar, Sudipta; Biswas, Paritosh Kumar; Mehedi, Hossain; Henning, Joerg; Mangtani, Punam; Flora, Meerjady Sabrina; Rahman, Mahmudur; Debnath, Nitish C.; Giasuddin, Mohammad; Barnett, Tony; Pfeiffer, Dirk U.; Fournié, Guillaume

  1. By: Ding, S.; Dziubinski, M.; Goyal, S.
    Abstract: A recurring theme in the study of society is the concentration of influence and power that is driven through unequal membership of groups and associations. In some instances these bodies constitute a small world while in others they are fragmented into distinct cliques. This paper presents a new model of clubs and networks to understand the sources of individual marginalization and the origins of different club networks. In our model, individuals seek to become members of clubs while clubs wish to have members. Club value is increasing in its size and in the strength of ties with other clubs. We show that a stable membership profile exhibits marginalization of individuals and that this is generally not welfare maximizing. Our second result shows that if returns from strength of ties are convex (concave) then stable memberships support fragmented networks with strong ties (small worlds held together by weak ties). We illustrate the value of these theoretical results through case studies of inter-locking directorates, boards of editors of journals, and defence and R&D alliances.
    Date: 2021–10–25
  2. By: Seabrook, Isobel E.; Barucca, Paolo; Caccioli, Fabio
    Abstract: To monitor risk in temporal financial networks, we need to understand how individual behaviours affect the global evolution of networks. Here we define a structural importance metric—which we denote as le—for the edges of a network. The metric is based on perturbing the adjacency matrix and observing the resultant change in its largest eigenvalues. We then propose a model of network evolution where this metric controls the probabilities of subsequent edge changes. We show using synthetic data how the parameters of the model are related to the capability of predicting whether an edge will change from its value of le. We then estimate the model parameters associated with five real financial and social networks, and we study their predictability. These methods have applications in financial regulation whereby it is important to understand how individual changes to financial networks will impact their global behaviour. It also provides fundamental insights into spectral predictability in networks, and it demonstrates how spectral perturbations can be a useful tool in understanding the interplay between micro and macro features of networks.
    Keywords: classification; dynamics; edge predictability; spectral perturbation
    JEL: F3 G3
    Date: 2021–12
  3. By: Pongou, Roland; Tchuente, Guy; Tondji, Jean-Baptiste
    Abstract: This study develops an economic model for a social planner who prioritizes health over short- term wealth accumulation during a pandemic. Agents are connected through a weighted undirected network of contacts, and the planner's objective is to determine the policy that contains the spread of infection below a tolerable incidence level, and that maximizes the present discounted value of real income, in that order of priority. The optimal unique policy depends both on the configuration of the contact network and the tolerable infection incidence. Comparative statics analyses are conducted: (i) they reveal the tradeoff between the economic cost of the pandemic and the infection incidence allowed; and (ii) they suggest a correlation between different measures of network centrality and individual lockdown probability with the correlation increasing with the tolerable infection incidence level. Using unique data on the networks of nursing and long-term homes in the U.S., we calibrate our model at the state level and estimate the tolerable COVID-19 infection incidence level. We find that laissez-faire (more tolerance to the virus spread) pandemic policy is associated with an increased number of deaths in nursing homes and higher state GDP growth. In terms of the death count, laissez-faire is more harmful to nursing homes than more peripheral in the networks, those located in deprived counties, and those who work for a profit. We also find that U.S. states with a Republican governor have a higher level of tolerable incidence, but policies tend to converge with high death count.
    Keywords: COVID-19,health-vs-wealth prioritization,economic cost,weighted networks,network centrality,nursing homes,optimally targeted lockdown policy
    JEL: D85 E61 H12 I18 J15
    Date: 2021
  4. By: Federico Guglielmo Morelli; Michael Benzaquen (LadHyX - Laboratoire d'hydrodynamique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Jean-Philippe Bouchaud; Marco Tarzia
    Abstract: We study a self-reflexive DSGE model with heterogeneous households, aimed at characterising the impact of economic recessions on the different strata of the society. Our framework allows to analyse the combined effect of income inequalities and confidence feedback mediated by heterogeneous social networks. By varying the parameters of the model, we find different crisis typologies: loss of confidence may propagate mostly within high income households, or mostly within low income households, with a rather sharp crossover between the two. We find that crises are more severe for segregated networks (where confidence feedback is essentially mediated between agents of the same social class), for which cascading contagion effects are stronger. For the same reason, larger income inequalities tend to reduce, in our model, the probability of global crises. Finally, we are able to reproduce a perhaps counter-intuitive empirical finding: in countries with higher Gini coefficients, the consumption of the lowest income households tends to drop less than that of the highest incomes in crisis times.
    Date: 2021–10–14
  5. By: Christian Esposito; Marco Gortan; Lorenzo Testa; Francesca Chiaromonte; Giorgio Fagiolo; Andrea Mina; Giulio Rossetti
    Abstract: "Success" of firms in venture capital markets is hard to define, and its determinants are still poorly understood. We build a bipartite network of investors and firms in the healthcare sector, describing its structure and its communities. Then, we characterize "success" introducing progressively more refined definitions, and we find a positive association between such definitions and the centrality of a company. In particular, we are able to cluster funding trajectories of firms into two groups capturing different "success" regimes and to link the probability of belonging to one or the other to their network features (in particular their centrality and the one of their investors). We further investigate this positive association by introducing scalar as well as functional "success" outcomes, confirming our findings and their robustness.
    Date: 2021–11
  6. By: Moyen, Natalie; Hoque, Md Ahasanul; Mahmud, Rashed; Hasan, Mahmudul; Sarkar, Sudipta; Biswas, Paritosh Kumar; Mehedi, Hossain; Henning, Joerg; Mangtani, Punam; Flora, Meerjady Sabrina; Rahman, Mahmudur; Debnath, Nitish C.; Giasuddin, Mohammad; Barnett, Tony; Pfeiffer, Dirk U.; Fournié, Guillaume
    Abstract: Live animal markets are known hotspots of zoonotic disease emergence. To mitigate those risks, we need to understand how networks shaped by trading practices influence disease spread. Yet, those practices are rarely recorded in high-risk settings. Through a large cross-sectional study, we assessed the potential impact of live poultry trading networks’ structures on avian influenza transmission dynamics in Bangladesh. Networks promoted mixing between chickens sourced from different farming systems and geographical locations, fostering co-circulation of viral strains of diverse origins in markets. Viral transmission models suggested that the observed rise in viral prevalence from farms to markets was unlikely explained by intra-market transmission alone, but substantially influenced by transmission occurring in upstream network nodes. Disease control interventions should therefore alter the entire network structures. However, as networks differed between chicken types and city supplied, standardised interventions are unlikely to be effective, and should be tailored to local structural characteristics.
    JEL: R14 J01
    Date: 2021–12

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