Artikel

Specification and estimation of network formation and network interaction models with the exponential probability distribution

We model network formation and interactions under a unified framework by considering that individuals anticipate the effect of network structure on the utility of network interactions when choosing links. There are two advantages of this modeling approach: first, we can evaluate whether network interactions drive friendship formation or not. Second, we can control for the friendship selection bias on estimated interaction effects. We provide microfoundations of this statistical model based on the subgame perfect equilibrium of a two-stage game and propose a Bayesian MCMC approach for estimating the model. We apply the model to study American high school students' friendship networks using the Add Health dataset. From two interaction variables, GPA and smoking frequency, we find that the utility of interactions in academic learning is important for friendship formation, whereas the utility of interactions in smoking is not. However, both GPA and smoking frequency are subject to significant peer effects.

Language
Englisch

Bibliographic citation
Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 11 ; Year: 2020 ; Issue: 4 ; Pages: 1349-1390 ; New Haven, CT: The Econometric Society

Classification
Wirtschaft
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
Analysis of Education
Fertility; Family Planning; Child Care; Children; Youth
Subject
Social networks
social interactions
selectivity
spatial autoregressive model
Bayesian estimation

Event
Geistige Schöpfung
(who)
Hsieh, Chih-Sheng
Lee, Lung-fei
Boucher, Vincent
Event
Veröffentlichung
(who)
The Econometric Society
(where)
New Haven, CT
(when)
2020

DOI
doi:10.3982/QE944
Handle
Last update
10.03.2025, 11:44 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Hsieh, Chih-Sheng
  • Lee, Lung-fei
  • Boucher, Vincent
  • The Econometric Society

Time of origin

  • 2020

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