Identification of network effects with spatially endogenous covariates: theory, simulations, and an empirical application

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985998%3A_____%2F25%3A00638658" target="_blank" >RIV/67985998:_____/25:00638658 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11640/25:00638659
Result on the web
<a href="https://doi.org/10.1080/07474938.2025.2514274" target="_blank" >https://doi.org/10.1080/07474938.2025.2514274</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/07474938.2025.2514274" target="_blank" >10.1080/07474938.2025.2514274</a>

Result language
angličtina
Original language name
Identification of network effects with spatially endogenous covariates: theory, simulations, and an empirical application
Original language description
Conventional methods for the estimation of peer, social, or network effects are invalid if individual unobservables and covariates correlate across observations. In this article, we characterize the identification conditions for consistently estimating all the parameters of a spatially autoregressive or linear-in-means model when the structure of social or peer effects is exogenous, but the observed and unobserved characteristics of agents are cross-correlated over some given metric space. We show that identification is possible if the network of social interactions is non overlapping up to enough degrees of separation and the spatial matrix that characterizes the co-dependence of individual unobservables and covariates is known up to a multiplicative constant. We propose a GMM approach for the estimation of the model’s parameters, and we evaluate its performance through Monte Carlo simulations. Finally, we revisit an empirical application about classmates in college. Contrasting with conventional methods, our methodology can estimate zero, non significant peer effects on both academic performance and major choice.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50202 - Applied Economics, Econometrics

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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