nep-net New Economics Papers
on Network Economics
Issue of 2018‒09‒03
seven papers chosen by
Pedro CL Souza
Pontifícia Universidade Católica do Rio de Janeiro

  1. Commodity Connectedness By Francis X. Diebold; Laura Liu; Kamil Yilmaz
  2. Consumption Network Effects By Giacomo De Giorgi
  3. Migration networks and Mexican migrants' spatial mobility in the US By Rebecca Lessem; Brian Cadena; Brian Kovak; Shan Li
  4. Peer effects in product adoption By Theresa Kuchler; Arlene Wong; Johannes Stroebel
  5. Credit Market Spillovers: Evidence from a Syndicated Loan Market Network By Abhimanyu Gupta; Alex Michaelides; Sotirios Kokas
  6. Immigrant Networks and Remittances: Cheaper together? By Ainhoa Aparicio Fenoll; Zoe Kuehn
  7. Incarceration Spillovers in Criminal and Family Networks By Manudeep Bhuller; Gordon B. Dahl; Katrine V. Løken; Magne Mogstad

  1. By: Francis X. Diebold (Department of Economics, University of Pennsylvania); Laura Liu (Federal Reserve Bank); Kamil Yilmaz (Department of Economics, Koç University)
    Abstract: We use variance decompositions from high-dimensional vector autoregressions to characterize connectedness in 19 key commodity return volatilities, 2011-2016. We study both static (full-sample) and dynamic (rolling-sample) connectedness. We summarize and visualize the results using tools from network analysis. The results reveal clear clustering of commodities into groups that match traditional industry groupings, but with some notable differences. The energy sector is most important in terms of sending shocks to others, and energy, industrial metals, and precious metals are themselves tightly connected.
    Keywords: network centrality, network visualization, pairwise connectedness, total directional connectedness, total connectedness, vector autoregression, variance decomposition, LASSO
    JEL: G1 C3
    Date: 2017–03–02
  2. By: Giacomo De Giorgi (GSEM University of Geneva)
    Abstract: In this paper we study consumption network e¤ects. Does the consumption of our peers a¤ect our own consumption? How large is such e¤ect? What are the economic mechanisms behind it? We use long panel data on the entire Danish population to construct a measure of consumption based on administrative tax records on income and assets. We combine tax record data with matched employer-employee data so that we can construct peer groups based on workplace, which gives us a much tighter, precise, and credible de nition of networks than used in previous literature. We use the available data to construct peer groups that do not perfectly overlap, and as such provide valid instruments derived from the network structure of ones peers group. The longitudinal nature of our data also allow us to estimate xed e¤ects models, which help us tackle reection, self-selection, and common-shocks issues all at once. We estimate non-negligible and statistically signi cant endogenous and exogenous peer e¤ects. Estimated e¤ects are quite relevant for policies as they generate non-negligible multiplier e¤ect. We also investigate what mechanisms generate such e¤ects, distinguishing between "keeping up with the Joneses", a status model, and a more traditional risk sharing view.
    Date: 2018
  3. By: Rebecca Lessem (Carnegie Mellon University); Brian Cadena (University of Colorado at Boulder); Brian Kovak (Carnegie Mellon University); Shan Li (Carnegie Mellon University)
    Abstract: Mexican low-skilled migrants are found to be highly mobile when they face labor demand shocks. This paper examines the role of migration networks in Mexican-born immigrants’ location choices. We rely on the sizable variation in labor demand declines across states during the Great Recession to identify migration responses to demand shocks and use a novel set of data, the Matrícula Consular de Alta Seguridad (MCAS) data, to construct migration network measures. We find that migration networks indeed play an important part in Mexican migrants’ responsiveness to local demand shocks.In particular, migrants respond to local economic conditions and conditions in network-connected locations when making location decisions.
    Date: 2018
  4. By: Theresa Kuchler (NYU Stern School of Business); Arlene Wong (Princeton University); Johannes Stroebel (New York University)
    Abstract: To what extend do friendship links affect the adoption of new products? We first combine social network data with individual choices on the adoption of the newly introduced Google Pixel phone to estimate the effects of friends’ adoption of a new product on individual purchasing decisions. We use the exclusive introduction of the Pixel by Verizon combined with differences in Verizon market share across the US to instrument for friends’ initial adoption of the phone. Next, we analyze the role of county level connections on the adoption of a wide variety of products in stores across US counties. Counties that are more connected socially adopt new products at similar times. We explore whether consumers in more socially connected counties have access to a more new products and a wider variety of products and the implications of such consumption externalities on consumer welfare.
    Date: 2018
  5. By: Abhimanyu Gupta (University of Essex); Alex Michaelides (Imperial College Business School); Sotirios Kokas (University of Essex)
    Abstract: A large theoretical literature emphasizes the importance of financial networks, but empirical studies remain scarce. Due to overlapping bank portfolios, the syndicated loan market provides a natural setting to study financial networks. We exploit the tiered structure of syndicated loans to construct such a network and characterize quantitatively its evolution over time. A spatial autoregressive model provides an ideal methodological framework to estimate spillovers from this financial network to lending rates and quantities. We find that these spillovers are economically large, time-varying and can switch sign after major economic shocks. Moreover, we find that network complexity and uncertainty rise after a large negative shock. Counterfactual experiments confirm the quantitative importance of spillovers and network structure on lending rates and quantities and can be used to disentangle the effects arising from spillovers versus changes in network structure.
    Date: 2018
  6. By: Ainhoa Aparicio Fenoll; Zoe Kuehn
    Abstract: We estimate the causal effects of immigrant networks on individuals' remittance sending behavior for migrants from many different countries residing in Spain. Our methodology addresses typical issues that arise when estimating network effects: reverse causality, common unobserved factors, and self-selection. In particular, we instrument the size of networks by predicting the number of migrants in each lo- cation using the location's accessibility by distinct methods of transportation and information about how migrants from each country arrived in Spain. Our findings show that immigrants from above-average remitting countries remit more if they live in larger networks. Testing for mechanisms of network e ects, we also find that these migrants are more likely to send remittances via bank transfers, which sug- gests that large networks of individuals who remit a lot might be better at sharing information about cheaper remittance channels (bank transfers compared to money orders in post offices or agencies). In line with this hypothesis, we find that due to network effects migrants shy away from the most expensive remittance channels, potentially freeing resources for additional remittances. Furthermore, cost spreads between the most expensive and cheapest providers are lower for countries charac- terized by high remittances and stronger networks, suggesting that network effects might be competition-enhancing.
    Keywords: immigrant networks, remittances, migration, Spain
    JEL: F24 J61 F22 O15 A14
    Date: 2017
  7. By: Manudeep Bhuller; Gordon B. Dahl; Katrine V. Løken; Magne Mogstad
    Abstract: Using quasi-random assignment of criminal cases to judges, we estimate large incarceration spillovers in criminal and brother networks. When a defendant is sent to prison, there are 51 and 32 percentage point reductions in the probability his criminal network members and younger brothers will be charged with a crime, respectively, over the ensuing four years. Correlational evidence misleadingly finds small positive effects. These spillovers are of first order importance for policy, as the network reductions in future crimes committed are larger than the direct effect on the incarcerated defendant.
    JEL: K42
    Date: 2018–08

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