An Econometric Model of Network Formation With Degree Heterogeneity

Graham, Bryan S.

doi:10.3982/ecta12679

Cited by 201 publications

(188 citation statements)

References 41 publications

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“…17 This model is very similar to a number of network formation models in the statistics and econometrics literature (see, e.g., Fafchamps and Gubert (2007), Graham (2017), McCormick 17 See Supplemental Material Appendix C for simulations that demonstrate this on empirical network data. 17 This model is very similar to a number of network formation models in the statistics and econometrics literature (see, e.g., Fafchamps and Gubert (2007), Graham (2017), McCormick 17 See Supplemental Material Appendix C for simulations that demonstrate this on empirical network data.…”

Section: What Kinds Of Network Exhibit Stuckness?mentioning

confidence: 70%

Testing Models of Social Learning on Networks: Evidence From Two Experiments

Chandrasekhar

Larreguy

Xandri

2020

ECTA

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We theoretically and empirically study an incomplete information model of social learning. Agents initially guess the binary state of the world after observing a private signal. In subsequent rounds, agents observe their network neighbors' previous guesses before guessing again. Agents are drawn from a mixture of learning types-Bayesian, who face incomplete information about others' types, and DeGroot, who average their neighbors' previous period guesses and follow the majority. We study (1) learning features of both types of agents in our incomplete information model;(2) what network structures lead to failures of asymptotic learning; (3) whether realistic networks exhibit such structures. We conducted lab experiments with 665 subjects in Indian villages and 350 students from ITAM in Mexico. We perform a reduced-form analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the Indian-villager and Mexican-student samples, respectively.A. G. CHANDRASEKHAR, H. LARREGUY, AND J. P. XANDRI information from their network neighbors when engaging in learning. This constraint may be for many reasons, including but not limited to operating in settings where learning is through observations of others' actions or the costs of communicating very complex information are too high and therefore only summaries are transmitted. 2 We study such a coarse communication environment. We focus on a setting in which individuals receive signals about an unknown binary state of the world in the first period and, in subsequent rounds, they communicate to their neighbors their best (binary) guess about the state of the world. 3 In this ubiquitous setting, if all agents are Bayesian, and there is common knowledge of this, under mild assumptions, learning will be asymptotically efficient in large networks (see Gale and Kariv (2003) and Mossel and Tamuz (2010) for a myopic learning environment and Mossel, Sly, and Tamuz (2015) for a strategic learning environment). However, if all agents update their guess as the majority of their neighbors' prior guesses-as modeled by a coarse DeGroot model of learning, also known as the majority voting model (Liggett (1985))-then it is possible that a non-trivial set of agents will end up stuck making the wrong guess. In practice, it might be that there is a mix of sophisticated (Bayesian) and naive (DeGroot) learners, and that Bayesians are aware of this and incorporate it in their calculations. Such an incomplete information model, its relevance, and implications for asymptotic learning have not been studied in our coarse communication environment.This paper develops an incomplete information model of social learning with coarse communication on a network in which agents can potentially be Bayesian or DeGroot, and agents have common knowledge of the distribution of Bayesian or DeGroot types in the population. Bayesian agents then learn in an environment of incomplete information. The model nests the two extreme cases-complete information all-Ba...

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Section: What Kinds Of Network Exhibit Stuckness?mentioning

confidence: 70%

Testing Models of Social Learning on Networks: Evidence From Two Experiments

Chandrasekhar

Larreguy

Xandri

2020

ECTA

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“…As has previously been noted (see, e.g., Graham, ; Kim, ), the fact that the econometrician does not observe A i or A j poses an important threat to identification in the context of our problem, as omitting these physician fixed effects can result in biased estimates of our parameters θ =( γ , β ). , Moreover, the sparsity of real‐world social networks makes it problematic to estimate the unobserved fixed effects directly because most agents within these networks have few links (low degree).…”

Section: Empirical Approachmentioning

confidence: 83%

The formation of physician patient sharing networks in medicare: Exploring the effect of hospital affiliation

Linde

2019

Health Economics

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This study explores the forces that drive the formation of physician patient sharing networks. In particular, I examine the degree to which hospital affiliation drives physicians' sharing of Medicare patients. Using a revealed preference framework where observed network links are taken to be pairwise stable, I estimate the physicians' pair‐specific values using a tetrad maximum score estimator that is robust to the presence of unobserved physician specific characteristics. I also control for a number of potentially confounding patient sharing channels, such as (a) common physician group or hospital system affiliation, (b) physician homophily, (c) knowledge complementarity, (d) patient side considerations related to both geographic proximity and insurance network participation, and (e) spillover from other collaborations. Focusing on the Chicago hospital referral region, I find that shared hospital affiliation accounts for 36.5% of the average pair‐specific utility from a link. Implications for reducing care fragmentation are discussed.

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“…3 Chandrasekhar and Jackson (2014) propose a different approach where the network is generated from overlapping sub-graphs. Also, some recent papers consider the estimation of dyadic link formation models (i.e., without link externalities) with a focus on disentangling homophily and node-specific heterogeneity (Graham 2017, Dzemski 2014.…”

Section: Introductionmentioning

confidence: 99%

“…1 See Graham (2015) and de Paula (forthcoming) for recent surveys. 2 Boucher and Mourifié (forthcoming) develop a method that is similar to Leung (2015) but bypasses the issue of multiplicity.…”

Section: Introductionmentioning

confidence: 99%

Identifying preferences in networks with bounded degree

Paula

Richards-Shubik

Tamer

2017

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This paper provides a framework for identifying preferences in a large network where links are pairwise stable. Network formation models present difficulties for identification, especially when links can be interdependent: e.g., when indirect connections matter. We show how one can use the observed proportions of various local network structures to learn about the underlying preference parameters. The key assumption for our approach restricts individuals to have bounded degree in equilibrium, implying a finite number of payoff-relevant local structures. Our main result provides necessary conditions for parameters to belong to the identified set. We then develop a quadratic programming algorithm that can be used to construct this set. With further restrictions on preferences, we show that our conditions are also sufficient for pairwise stability and therefore characterize the identified set precisely. Overall, the use of both the economic model along with pairwise stability allows us to obtain effective dimension reduction.

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An Econometric Model of Network Formation With Degree Heterogeneity

Cited by 201 publications

References 41 publications

Testing Models of Social Learning on Networks: Evidence From Two Experiments

Testing Models of Social Learning on Networks: Evidence From Two Experiments

The formation of physician patient sharing networks in medicare: Exploring the effect of hospital affiliation

Identifying preferences in networks with bounded degree

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