nep-net New Economics Papers
on Network Economics
Issue of 2018‒12‒17
three papers chosen by
Pedro CL Souza
Pontifícia Universidade Católica do Rio de Janeiro

  1. Non-Randomly Sampled Networks: Biases and Corrections By Chih-Sheng Hsieh; Stanley I. M. Ko; Jaromír Kovářík; Trevon Logan
  2. Leveraging Patients' Social Networks to Overcome Tuberculosis Underdetection: A Field Experiment in India By Jessica Goldberg; Mario Macis; Pradeep Chintagunta
  3. Endogenous Technology Cycles in Dynamic R&D Networks By König, Michael; Rogers, Tim

  1. By: Chih-Sheng Hsieh; Stanley I. M. Ko; Jaromír Kovářík; Trevon Logan
    Abstract: This paper analyzes statistical issues arising from non-representative network samples of the population, the most common network data used. We first characterize the biases in both network statistics and estimates of network effects under non-random sampling theoretically and numerically. Sampled network data systematically bias the properties of observed networks and suffer from non-classical measurement-error problems if applied as regressors. Apart from the sampling rate and the elicitation procedure, these biases depend in a non-trivial way on which subpopulations are missing with higher probability. We then propose a methodology, adapting post-stratification weighting approaches to networked contexts, which enables researchers to recover several network-level statistics and reduce the biases in the estimated network effects. The advantages of the proposed methodology are that it can be applied to network data collected via both designed and non-designed sampling procedures, does not require one to assume any network formation model, and is straightforward to implement. We use Monte Carlo simulation and two widely used empirical network data sets to show that accounting for the non-representativeness of the sample dramatically changes the results of regression analysis.
    JEL: C4 D85 L14 Z13
    Date: 2018–11
  2. By: Jessica Goldberg; Mario Macis; Pradeep Chintagunta
    Abstract: Peer referrals are a common strategy for addressing asymmetric information in contexts such as the labor market. They could be especially valuable for increasing testing and treatment of infectious diseases, where peers may have advantages over health workers in both identifying new patients and providing them credible information, but they are rare in that context. In an experiment with 3,182 patients at 128 tuberculosis (TB) treatment centers in India, we find peers are indeed more effective than health workers in bringing in new suspects for testing, and low-cost incentives of about $US 3 per referral considerably increase the probability that current patients make referrals that result in the testing of new symptomatics and the identification of new TB cases. Peer outreach identifies new TB cases at 25%-35% of the cost of outreach by health workers and can be a valuable tool in combating infectious disease.
    JEL: I1 O1
    Date: 2018–11
  3. By: König, Michael; Rogers, Tim
    Abstract: We study the coevolutionary dynamics of knowledge creation and diffusion with the formation of R&D collaboration networks. Differently to previous works, we do not treat knowledge as an abstract scalar variable, but rather represent it as a multidimensional portfolio of technologies. Over time the composition of this portfolio may change due innovations and knowledge spillovers between collaborating firms. The collaborations between firms, in turn, are dynamically adjusted based on the firms' expectations of learning a new technology from their collaboration partners. We show that the interplay between knowledge diffusion, network formation and competition across sectors can give rise to a cyclical pattern in the collaboration intensity, which can be described as a damped oscillation. This theoretical finding recapitulates the novel observation of oscillations in an empirical sample of a large R&D collaboration network over several decades. Finally, we apply our findings to describe how an effective R&D policy can balance subsidies for entrants as well as R&D collaborations between incumbent firms.
    Keywords: Innovation; network formation; R&D networks; technology cycles
    JEL: D85 L24 O32 O33
    Date: 2018–11

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