nep-net New Economics Papers
on Network Economics
Issue of 2018‒08‒27
four papers chosen by
Pedro CL Souza
Pontifícia Universidade Católica do Rio de Janeiro

  1. On the Direct and Indirect Real Effects of Credit Supply Shocks By Laura Alfaro; Enrique Moral-Benito; Manuel Garcia-Santana
  2. Social Interaction and Technology Adoption: Experimental Evidence from Improved Cookstoves in Mali By Bonan, Jacopo; Battiston, Pietro; Bleck, Jaimie; LeMay-Boucher, Philippe; Pareglio, Stefano; Sarr, Bassirou; Tavoni, Massimo
  3. Testing for Peer Effects Using Genetic Data By Cawley, J.;; Han, E.;; Kim, J.;; Norton, E.C.;
  4. Estimating Models with Dynamic Network Interactions and Unobserved Heterogeneity By Luisa Corrado; Salvatore Di Novo

  1. By: Laura Alfaro (Harvard Business School); Enrique Moral-Benito (Bank of Spain); Manuel Garcia-Santana (Universitat Pompeu Fabra)
    Abstract: We consider the real effects of bank lending shocks and how they permeate the economy through buyer-supplier linkages. We combine administrative data on all firms in Spain with a matched bank-firm-loan dataset incorporating information on the universe of corporate loans for 2003-2013. Using methods from the matched employer-employee literature for handling large data sets, we identify bank-specific shocks for each year in our sample. Combining the Spanish Input-Output structure and firm-specific measures of upstream and downstream exposure, we construct firm-specific exogenous credit supply shocks and estimate their direct and indirect effects on real activity. Credit supply shocks have sizable direct and downstream propagation effects on investment and output throughout the period but no significant impact on employment during the expansion period. Downstream propagation effects are quantitatively larger in magnitude than direct effects. The results corroborate the importance of network effects in quantifying the real effects of credit shocks and show that real effects vary during booms and busts.
    Date: 2018
  2. By: Bonan, Jacopo; Battiston, Pietro; Bleck, Jaimie; LeMay-Boucher, Philippe; Pareglio, Stefano; Sarr, Bassirou; Tavoni, Massimo
    Abstract: We investigate the role of social interaction in technology adoption by conducting a field experiment in neighborhoods of Bamako. We invited women to attend a training/marketing session, where information on a more efficient cooking stove was provided and the chance to purchase the product at market price was offered. We randomly provided an information nudge on a peer’s willingness to buy an improved cookstove. We find that women purchase and use the product more when they receive information on a peer who purchased (or previously owned) the product, particularly if she is viewed as respected. In general, we find positive direct and spillover effects of attending the session. We also investigate whether social interaction plays a role in technology diffusion. We find that women who participated in the session, but did not buy during the intervention, are more likely to adopt the product when more women living around them own it. We investigate the mechanisms and provide evidence supporting imitation effects, rather than social learning or constraint interaction.
    Keywords: Research and Development/Tech Change/Emerging Technologies
    Date: 2017–09–25
  3. By: Cawley, J.;; Han, E.;; Kim, J.;; Norton, E.C.;
    Abstract: This paper tests for peer effects in obesity in a novel way. It addresses the reflection problem by using the alter’s genetic risk score for obesity, which is a significant predictor of obesity, is determined prior to birth, and cannot be affected by the behavior of others. It addresses the endogeneity of peer group formation by examining peers who are not self-selected: full siblings. We find evidence of positive peer effects in weight and obesity; having a sibling with a high genetic predisposition to obesity raises one’s risk of obesity, even controlling for one’s own genetic predisposition to obesity.
    Keywords: peer effects; obesity; genetics;
    JEL: I1 I12 I18 D1 J1 Z18
    Date: 2018–08
  4. By: Luisa Corrado (DEF & CEIS,University of Rome "Tor Vergata"); Salvatore Di Novo (University of Rome "Tor Vergata")
    Abstract: In this paper, we propose an approach to estimate models with network interactions in the presence of individual unobserved heterogeneity. The latter may impact the formation of ties and/or exogenous effects, thereby undermining identification of the associated parameters. In a panel setting, we devise a way to cope with these sources of endogeneity by relying on observable variations. When exogenous effect are involved, one can control for unobserved heterogeneity by including time-averages of the endogenous variables. When unobserved individual traits affect the process of network formation, it is possible to explore the role of network statistics. We derive a 2SLS estimator in order to address simultaneity bias, relying on sources of variation provided by the product between successive powers of the network matrix and the matrix of exogenous covariates; we assess the performances of the method via a Monte Carlo exercise, considering various combination of models and different ranges of parameters for both network interactions and the social multiplier. We also separately assess the cases in which unobserved sources hit the network structure only or act on exogenous effects as well. Focusing on the former case, our approach may be also applied when a simple cross-section is available. More generally, it does not require full knowledge of the spectrum of agents' interactions.
    Keywords: Networks,Individual Unobserved Heterogeneity,Dynamic Network Formation,network Statistics.
    JEL: C31 C36
    Date: 2018–08–09

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