nep-net New Economics Papers
on Network Economics
Issue of 2020‒04‒13
ten papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. The Speed of Innovation Diffusion in Social Networks* By Itai Arieli; Yakov Babichenko; Ron Peretz; H. Peyton Young
  2. The Network Dynamics of Social and Technological Conventions By Joshua Becker
  3. Network compatibility, intensity of competition and process R&D: A Generalization By Sumit Shrivastav
  4. Proximity, Innovation and Networks: A Concise Review and Some Next Steps By Pierre-Alexandre Balland; Ron Boschma; Koen Frenken
  5. Distance in Bank Lending: The Role of Social Networks By Oliver Rehbein; Simon Rother
  6. Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework By Benjamin Avanzi; Greg Taylor; Bernard Wong; Alan Xian
  7. Calling from the outside: The role of networks in residential mobility By Konstantin Büchel; Maximilian V. Ehrlich; Diego Puga; Elisabet Viladecans-Marsal
  8. Key Players in Economic Development By Amarasinghe, Ashani; Hodler, Roland; Raschky, Paul A.; Zenou, Yves
  9. Robust Opinion Aggregation and its Dynamics By Simone Cerreia-Vioglio; Roberto Corrao; Giacomo Lanzani
  10. Modelling Network Interference with Multi-valued Treatments: the Causal Effect of Immigration Policy on Crime Rates By C. Tort\`u; L. Forastiere; I. Crimaldi; F. Mealli

  1. By: Itai Arieli (Faculty of Industrial Engineering and Management, Technion–Israel Institute of Technology); Yakov Babichenko (Faculty of Industrial Engineering and Management, Technion–Israel Institute of Technology); Ron Peretz (Department of Economics, Bar Ilan University); H. Peyton Young (London School of Economics and Nuffield College, University of Oxford)
    Abstract: New ways of doing things often get started through the actions of a few innovators, then diffuse rapidly as more and more people come into contact with prior adopters in their social network. Much of the literature focuses on the speed of diffusion as a function of the network topology. In practice, however, the topology may not be known with any precision, and it is constantly in flux as links are formed and severed. Here we establish an upper bound on the expected waiting time until a given proportion of the population has adopted that holds independently of the network structure. Kreindler and Young [33, 2014] demonstrated such a bound for regular networks when agents choose between two options: the innovation and the status quo. Our bound holds for directed and undirected networks of arbitrary size and degree distribution, and for multiple competing innovations with different payoffs.
    Date: 2019–08–22
  2. By: Joshua Becker
    Abstract: When innovations compete for adoption, chance historical events can allow an inferior strategy to spread at the expense of superior alternatives. However, advantage is not always due to chance, and networks have emerged as an important determinant of organizational behavior. To understand what factors can impact the likelihood that the best alternative will be adopted, this paper asks: how does network structure shape the emergence of social and technological conventions? Prior research has found that highly influential people, or "central" nodes, can be beneficial from the perspective of a single innovation because promotion by central nodes can increase the speed of adoption. In contrast, when considering the competition of multiple strategies, the presence of central nodes may pose a risk, and the resulting "centralized" networks are not guaranteed to favor the optimal strategy. This paper uses agent-based simulation to investigate the effect of network structure on a standard model of convention formation, finding that network centralization increases the speed of convention formation but also decreases the likelihood that the best strategy will become widely adopted. Surprisingly, this finding does not indicate a speed/optimality trade-off: dense networks are both fast and optimal.
    Date: 2020–03
  3. By: Sumit Shrivastav (Indira Gandhi Institute of Development Research)
    Abstract: This paper analyses implications of network compatibility and competition on process innovation in differentiated network goods duopoly. It shows that firms R&D investments are strategic substitutes (complements), if effective network compatibility is less (more) than product substitutability, regardless of the nature of competition. If R&D investments are strategic complements, firms always invest in process innovation and they invest more under Bertrand competition than under Cournot competition. If R&D investments are strategic substitutes, unlike Cournot firms, Bertrand firms dont always undertake process innovation; but, when Bertrand firms also undertake process innovation, Cournot-Bertrand R&D ranking depends on the strength of network externalities.
    Keywords: Network compatibility, Network Externalities, Process R&D, Bertrand-Cournot Compari- son, Product Differentiation
    JEL: L13 D43 O31
    Date: 2020–02
  4. By: Pierre-Alexandre Balland; Ron Boschma; Koen Frenken
    Abstract: We review proximity research on collaborative innovation among organizations. We discuss the basic theorical tenets of collaborative innivation and summarize empirical findings on the roles of various forms of proximity. At the aggregative level, we look at studies of inter-organizational relations at the aggregate level of innovation systems. We end with a discussion of next steps in proximity research on collaborative innovation.
    Keywords: proximity, innovation, networks, inter-organizational relations, innovation systems, knowledge base
    JEL: B25 D85 L14 O3 R1
    Date: 2020–03
  5. By: Oliver Rehbein; Simon Rother
    Abstract: This paper provides empirical evidence that banks leverage social connections as an information channel. Using county-to-county friendship-link data from Facebook, we find that strong social ties increase loan volumes, especially if screening incentives are large. This effect is distinct from physical and cultural distances. Physical distance becomes significantly less relevant when accounting for social connections. Moreover, sufficiently strong social ties prevent cultural differences from constituting a lending barrier. The effect of social connectedness is more supply-side driven for small banks but demand-side driven for large banks. To bolster identification, we exploit highway connections, historical travel costs, and the quasi-random staggered introduction of Facebook as instruments. Our results reveal the important role of social connectedness as an information channel, speak to the nature of borrowing constraints, and point toward implications for bank-lending strategies and anti-trust policies.
    Keywords: bank lending, social networks, information frictions, culture, distance
    JEL: D82 D83 G21 O16 L14 Z13
    Date: 2020–03
  6. By: Benjamin Avanzi; Greg Taylor; Bernard Wong; Alan Xian
    Abstract: The Markov-modulated Poisson process is utilised for count modelling in a variety of areas such as queueing, reliability, network and insurance claims analysis. In this paper, we extend the Markov-modulated Poisson process framework through the introduction of a flexible frequency perturbation measure. This contribution enables known information of observed event arrivals to be naturally incorporated in a tractable manner, while the hidden Markov chain captures the effect of unobservable drivers of the data. In addition to increases in accuracy and interpretability, this method supplements analysis of the latent factors. Further, this procedure naturally incorporates data features such as over-dispersion and autocorrelation. Additional insights can be generated to assist analysis, including a procedure for iterative model improvement. Implementation difficulties are also addressed with a focus on dealing with large data sets, where latent models are especially advantageous due the large number of observations facilitating identification of hidden factors. Namely, computational issues such as numerical underflow and high processing cost arise in this context and in this paper, we produce procedures to overcome these problems. This modelling framework is demonstrated using a large insurance data set to illustrate theoretical, practical and computational contributions and an empirical comparison to other count models highlight the advantages of the proposed approach.
    Date: 2020–03
  7. By: Konstantin Büchel (University of Bern); Maximilian V. Ehrlich (University of Bern); Diego Puga (CEMFI, Centro de Estudios Monetarios y Financieros); Elisabet Viladecans-Marsal (Universitat de Barcelona)
    Abstract: Using anonymised cellphone data, we study the role of social networks in residential mobility decisions. Individuals with few local contacts are more likely to change residence. Movers strongly prefer places with more of their contacts closeby. Contacts matter because proximity to them is itself valuable and increases the enjoyment of attractive locations. They also provide hard-to-find local information and reduce frictions, especially in home-search. Local contacts who left recently or are more central are particularly influential. As people age, proximity to family gains importance relative to friends.
    Keywords: Social networks, residential mobility.
    JEL: R23 L14
    Date: 2019–03
  8. By: Amarasinghe, Ashani (Monash University); Hodler, Roland (University of St. Gallen); Raschky, Paul A. (Monash University); Zenou, Yves (Monash University)
    Abstract: This paper analyzes the role of networks in the spatial diffusion of local economic shocks in Africa. We show that road and ethnic connectivity are particularly important factors for diffusing economic spillovers over longer distances. We then determine the key players, i.e., which districts are key in propagating local economic shocks across Africa. Using these results, we conduct counterfactual policy exercises to evaluate the potential gains from policies that increase economic activity in specific districts or improve road connectivity between districts.
    Keywords: transportation, natural resources, key player centrality, spatial spillovers, networks, Africa
    JEL: O13 O55 R12
    Date: 2020–03
  9. By: Simone Cerreia-Vioglio; Roberto Corrao; Giacomo Lanzani
    Abstract: We study agents in a social network who receive initial noisy signals about a fundamental parameter and then, in each period, solve a robust non-parametric estimation problem given their previous information and the most recent estimates of their neighbors. The resulting robust opinion aggregators are characterized by simple functional properties: normalization, monotonicity, and translation invariance. These aggregators admit the linear DeGroot's model as a particular parametric specification. However, robust opinion aggregators allow for additional features such as overweighting/underweighting of extreme opinions, confirmatory bias, as well as discarding information obtained from sources perceived as redundant. We show that under this general model, it is still possible to link the long-run behavior of the opinions to the structure of the underlying network. In particular, we provide sufficient conditions for convergence and consensus and we offer some bounds on the rate of convergence. In some parametric cases, we derive the influence of the agents on the limit opinions and we stress how it depends on their centrality as well as on their initial signals. Finally, we study sufficient conditions under which a large society learns the true parameter while also highlighting why this property may fail.
    Date: 2020
  10. By: C. Tort\`u; L. Forastiere; I. Crimaldi; F. Mealli
    Abstract: Policy evaluation studies, which aim to assess the effect of an intervention, imply some statistical challenges: real-world scenarios provide treatments which have not been assigned randomly and the analysis might be further complicated by the presence of interference between units. Researchers have started to develop novel methods that allow to manage spillover mechanisms in observational studies, under binary treatments. But many policy evaluation studies require complex treatments, such as multi-valued treatments. For instance, in political sciences, evaluating the impact of policies implemented by administrative entities often implies a multi-valued approach, as the general political stance towards a specific issue varies over many dimensions. In this work, we extend the statistical framework about causal inference under network interference in observational studies, allowing for a multi-valued individual treatment and an interference structure shaped by a weighted network. Under multi-valued treatment, each unit is exposed to all levels of the treatment, due to the influence of his neighbors, according to the network weights. The estimation strategy is based on a joint multiple generalized propensity score and allows to estimate direct effects, controlling for both individual and network covariates. We follow the proposed methodology to analyze the impact of national immigration policy on crime rates. We define a multi-valued characterization of political attitudes towards migrants and we assume that the extent to which each country can be influenced by another is modeled by an appropriate indicator, that we call Interference Compound Index (ICI). Results suggest that implementing highly restrictive immigration policies leads to an increase of crime rates and the magnitude of estimated effects is stronger if we take into account multi-valued interference.
    Date: 2020–02

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