nep-net New Economics Papers
on Network Economics
Issue of 2019‒02‒11
five papers chosen by
Pedro CL Souza
Pontifícia Universidade Católica do Rio de Janeiro

  1. Naive Learning with Uninformed Agents By Abhijit Banerjee; Emily Breza; Arun G. Chandrasekhar; Markus Mobius
  2. Professional networks and their coevolution with executive careers: Evidence from North America and Europe By Berardi, Nicoletta; Lalanne, Marie; Seabright, Paul
  3. Broadband Internet and Social Capital By Andrea Geraci; Mattia Nardotto; Tommaso Reggiani; Fabio Sabatini
  4. Chinese competition and network effects on the extensive margin By Daniel Goya
  5. National Industry Trade Shocks, Local Labor Markets, and Agglomeration Spillovers By Helm, Ines

  1. By: Abhijit Banerjee; Emily Breza; Arun G. Chandrasekhar; Markus Mobius
    Abstract: The DeGroot model has emerged as a credible alternative to the standard Bayesian model for studying learning on networks, offering a natural way to model naive learning in a complex setting. One unattractive aspect of this model is the assumption that the process starts with every node in the network having a signal. We study a natural extension of the DeGroot model that can deal with sparse initial signals. We show that an agent's social influence in this generalized DeGroot model is essentially proportional to the number of uninformed nodes who will hear about an event for the first time via this agent. This characterization result then allows us to relate network geometry to information aggregation. We identify an example of a network structure where essentially only the signal of a single agent is aggregated, which helps us pinpoint a condition on the network structure necessary for almost full aggregation. We then simulate the modeled learning process on a set of real world networks; for these networks there is on average 21.6% information loss. We also explore how correlation in the location of seeds can exacerbate aggregation failure. Simulations with real world network data show that with clustered seeding, information loss climbs to 35%.
    JEL: D8 D83 D85 O1 O12 Z13
    Date: 2019–01
  2. By: Berardi, Nicoletta; Lalanne, Marie; Seabright, Paul
    Abstract: This paper examines how networks of professional contacts contribute to the development of the careers of executives of North American and European companies. We build a dynamic model of career progression in which career moves may both depend upon existing networks and contribute to the development of future networks. We test the theory on an original dataset of nearly 73 000 executives in over 10 000 firms. In principle professional networks could be relevant both because they are rewarded by the employer and because they facilitate job mobility. Our econometric analysis suggests that, although there is a substantial positive correlation between network size and executive compensation, with an elasticity of around 20%, almost all of this is due to unobserved individual characteristics. The true causal impact of networks on compensation is closer to an elasticity of 1 or 2% on average, all of this due to enhanced probability of moving to a higher-paid job. And there appear to be strongly diminishing returns to network size.
    Keywords: professional networks,labor mobility,executive compensation
    JEL: D85 J31 J62 M12
    Date: 2018
  3. By: Andrea Geraci (European Commission JRC); Mattia Nardotto (KU Leuven); Tommaso Reggiani (Masaryk University); Fabio Sabatini (Sapienza University of Rome)
    Abstract: We study how the diffusion of broadband Internet affects social capital using two data sets from the UK. Our empirical strategy exploits the fact that broadband access has long depended on customers’ position in the voice telecommunication infrastructure that was designed in the 1930s. The actual speed of an Internet connection, in fact, rapidly decays with the distance of the dwelling from the specific node of the network serving its area. Merging unique information about the topology of the voice network with geocoded longitudinal data about individual social capital, we show that access to broadband Internet caused a significant decline in forms of offline interaction and civic engagement. Overall, our results suggest that broadband penetration substantially crowded out several aspects of social capital.
    Keywords: ICT, broadband infrastructure, networks, Internet, social capital, civic capital
    JEL: C91 D9 D91 Z1
    Date: 2018–12
  4. By: Daniel Goya
    Abstract: I construct a network of input-output linkages in Chilean manufacturing and show that a negative demand shock has an impact on the number of firms producing in sectors that supply the sectors affected by the shock. Approximately one-third of the effect of increased Chinese competition on the extensive margin can be attributed to these network effects. The observed effect is a combination of multiproduct firms dropping varieties and firms leaving the market. I also study whether there is evidence of 'cascading failures' that could amplify the impact of idiosyncratic shocks. I find no evidence of these 'cascading effects'.
    Keywords: production networks, extensive margin, propagation of shocks, input-output, Chinese competition.
    JEL: D57 L25
    Date: 2019–02
  5. By: Helm, Ines (Dept. of Economics, Stockholm University)
    Abstract: Using a broad set of national industry trade shocks, I employ a novel approach to estimate agglomeration effects by exploiting within industry variation in indirect exposure to the other local industries’ (national) trade shocks across local labor markets. This variation stems from differences in local industry composition and allows to test for the existence of heterogeneous agglomeration effects across industries. I find considerable employment spillovers from other tradable industries’ trade shocks and even stronger effects within the same broad sector. Spillovers are larger for industries employing similar workers and are triggered predominantly by shocks to high technology industries.
    Keywords: Agglomeration; Local Labor Markets; Trade Shocks
    JEL: F16 J20 R11 R12
    Date: 2019–01–10

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