nep-net New Economics Papers
on Network Economics
Issue of 2018‒03‒19
three papers chosen by
Pedro CL Souza
Pontifícia Universidade Católica do Rio de Janeiro

  1. Seeing the Forest for the Trees? An Investigation of Network Knowledge By Emily Breza; Arun G. Chandrasekhar; Alireza Tahbaz-Salehi
  2. Structural changes in the interbank market across the financial crisis from multiple core-periphery analysis By Sadamori Kojaku; Giulio Cimini; Guido Caldarelli; Naoki Masuda
  3. Explaining the structure of collaboration networks: from firm-level strategies to global network structure By Johannes van der Pol

  1. By: Emily Breza; Arun G. Chandrasekhar; Alireza Tahbaz-Salehi
    Abstract: This paper assesses the empirical content of one of the most prevalent assumptions in the economics of networks literature, namely the assumption that decision makers have full knowledge about the networks they interact on. Using network data from 75 villages, we ask 4,554 individuals to assess whether five randomly chosen pairs of households in their village are linked through financial, social, and informational relationships. We find that network knowledge is low and highly localized, declining steeply with the pair’s network distance to the respondent. 46% of respondents are not even able to offer a guess about the status of a potential link between a given pair of individuals. Even when willing to offer a guess, respondents can only correctly identify the links 37% of the time. We also find that a one-step increase in the social distance to the pair corresponds to a 10pp increase in the probability of misidentifying the link. We then investigate the theoretical implications of this assumption by showing that the predictions of various models change substantially if agents behave under the more realistic assumption of incomplete knowledge about the network. Taken together, our results suggest that the assumption of full network knowledge (i) may serve as a poor approximation to the real world and (ii) is not innocuous: allowing for incomplete network knowledge may have first-order implications for a range of qualitative and quantitative results in various contexts.
    JEL: C8 D8 D85 L14 O1
    Date: 2018–02
  2. By: Sadamori Kojaku; Giulio Cimini; Guido Caldarelli; Naoki Masuda
    Abstract: Interbank markets are often characterised in terms of a core-periphery network structure, with a highly interconnected core of banks holding the market together, and a periphery of banks connected mostly to the core but not internally. This paradigm has recently been challenged for short time scales, where interbank markets seem better characterised by a bipartite structure with more core-periphery connections than inside the core. Using a novel core-periphery detection method on the eMID interbank market, we enrich this picture by showing that the network is actually characterised by multiple core-periphery pairs. Moreover, a transition from core-periphery to bipartite structures occurs by shortening the temporal scale of data aggregation. We further show how the global financial crisis transformed the market, in terms of composition, multiplicity and internal organisation of core-periphery pairs. By unveiling such a fine-grained organisation and transformation of the interbank market, our method can find important applications in the understanding of how distress can propagate over financial networks.
    Date: 2018–02
  3. By: Johannes van der Pol
    Abstract: The aim of this paper is to show how firm-level partner selection strategies impact the structure a of collaboration network. The analysis is performed in three stages. A first stage identifies how partners select their collaborators, a second stage shows how these decisions result in clusters, and a final stage studies the global network structure that emerges from the interconnection of these clusters. In order to highlight the importance of the sectors’ influence, the analysis is performed on the French Aerospace and the French Biotech collaboration networks. Results show that the firm-level strategies are the same in both sectors while the resulting global network structure is different (core-periphery structure with small-world characteristics for the aerospace network and no particular structure for the biotech sector). The difference in the global network structure can be explained by sectorial characteristics. These differences define the manner in which knowledge flows through the network.
    Keywords: SNA; Sectoral analysis; Collaboration network; Biotechnology; Aerospace; ERGM; Innovation
    JEL: L25 C23 D85 L14 C20
    Date: 2018

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