nep-net New Economics Papers
on Network Economics
Issue of 2019‒04‒01
four papers chosen by
Pedro CL Souza
Pontifícia Universidade Católica do Rio de Janeiro

  1. Migration and the Value of Social Networks By Blumenstock, Joshua; Chi, Guanghua; Tan, Xu
  2. Identification and Estimation of a Partially Linear Regression Model using Network Data By Eric Auerbach
  3. The network origins of the gains from trade By Maarten Bosker; Bastian Westbrock
  4. Calling from the outside: The role of networks in residential mobility By Buechel, Konstantin; Puga, Diego; Viladecans-Marsal, Elisabet; von Ehrlich, Maximilian

  1. By: Blumenstock, Joshua; Chi, Guanghua; Tan, Xu
    Abstract: What is the value of a social network? Prior work suggests two distinct mechanisms that have historically been difficult to differentiate: as a conduit of information, and as a source of social and economic support. We use a rich 'digital trace' dataset to link the migration decisions of millions of individuals to the topological structure of their social networks. We find that migrants systematically prefer 'interconnected' networks (where friends have common friends) to 'expansive' networks (where friends are well connected). A micro-founded model of network-based social capital helps explain this preference: migrants derive more utility from networks that are structured to facilitate social support than from networks that efficiently transmit information.
    Keywords: Big Data; Development; migration; networks; social capital; Social Networks
    JEL: D85 O12 O15 R23 Z13
    Date: 2019–03
  2. By: Eric Auerbach
    Abstract: I study a regression model in which one covariate is an unknown function of a latent driver of link formation in a network. Rather than specify or fit a parametric network formation model, I introduce a new method based on matching pairs of agents with similar columns of the squared adjacency matrix, the ijth entry of which contains the number of other agents linked to both agents i and j. The intuition behind this approach is that for a large class of network formation models the columns of this matrix characterize all of the identifiable information about individual linking behavior. In the paper, I first describe the model and formalize this intuition. I then introduce estimators for the parameters of the regression model and characterize their large sample properties.
    Date: 2019–03
  3. By: Maarten Bosker; Bastian Westbrock
    Abstract: In this paper, we develop a network perspective on the welfare gains from trade in today’s internationally fragmented supply chains. Towards this end, we study a Ricardian trade model featuring trade in final and intermediate products, and introduce a novel comparative statics approach to decompose the total welfare effects of an arbitrary trade cost shock into several meaningful, easily quantifiable, components. This approach uncovers a unique feature of supply chain trade: the gains from trade are not so much determined by a country's own access to the technologies and markets of its direct trading partners, but rather by its supply chain exposure to countries further up- and downstream in the global supply chain. We develop a set of simple statistics to measure each country’s supply chain exposure, show how it predicts the gains from trade, and identify each country's key trade intermediaries, i.e., other nations that primarily leverage its supply chain exposure.
    Keywords: global supply chains, gains from trade, network diffusion, network exposure, trade intermediation
    JEL: F10 F11
    Date: 2019
  4. By: Buechel, Konstantin; Puga, Diego; Viladecans-Marsal, Elisabet; von Ehrlich, Maximilian
    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 close-by. 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: cellphone data; residential mobility; Social Networks
    JEL: R23
    Date: 2019–03

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