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A DECOMPOSITION ANALYSIS OF DIFFUSION OVER A LARGE NETWORK

Published online by Cambridge University Press:  11 July 2022

Kyungchul Song
Kyungchul Song*
Affiliation:
University of British Columbia
*
Address correspondence to Kyungchul Song, Vancouver School of Economics, University of British Columbia, 6000 Iona Drive, Vancouver, BC V6T 1L4, Canada; e-mail: [email protected].
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Abstract

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Diffusion over a network refers to the phenomenon of a change of state of a cross-sectional unit in one period leading to a change of state of its neighbors in the network in the next period. One may estimate or test for diffusion by estimating a cross-sectionally aggregated correlation between neighbors over time from data. However, the estimated diffusion can be misleading if the diffusion is confounded by omitted covariates. This paper focuses on the measure of diffusion proposed by He and Song (2022, Preprint, arXiv:1812.04195v4 [stat.ME]), provides a method of decomposition analysis to measure the role of the covariates on the estimated diffusion, and develops an asymptotic inference procedure for the decomposition analysis in such a situation. This paper also presents results from a Monte Carlo study on the small sample performance of the inference procedure.

Type
ARTICLES
Information
Econometric Theory , Volume 38 , Issue 6: YALE 2018 CONFERENCE IN HONOR OF PETER C. B. PHILLIPS: PART II , December 2022 , pp. 1221 - 1252
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Footnotes

I thank Mahdi Ebrahimi Kahou for his valuable comments at the beginning of this research, and Yige Duan for excellent assistance in this research, including numerous helpful comments on this work. I also thank the Co-Editor and two anonymous referees for criticisms and suggestions. I acknowledge that this research was supported by the Social Sciences and Humanities Research Council of Canada.

References

REFERENCES

Acemoglu, D., Ozdaglar, A., & Yildiz, E. (2011) Diffusion of innovations in social networks. In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), 12/2011 , pp. 2329–2334 Institute of Electrical and Electronics Engineers (IEEE).Google Scholar
Akbarpour, M., Malladi, S., & Saberi, A. (2020) Just a Few Seeds More: Value of Network Information for Diffusion. Working paper.Google Scholar
Aral, S., Muchnik, L., & Sundararajan, A. (2009) Distinguishing influence-based contagion from Homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences of the United States of America 106(51), 21544–21549.CrossRefGoogle ScholarPubMed
Aronow, P. & Samii, C. (2017) Estimating average causal effects under general interference, with application to a social network experiment. Annals of Applied Statistics 11, 1912–1947.CrossRefGoogle Scholar
Banerjee, A., Chandrasekhar, A.G., Duflo, E., & Jackson, M.O. (2013) The diffusion of microfinance. Science 341(6144), 1236498.CrossRefGoogle ScholarPubMed
Banerjee, A., Chandrasekhar, A.G., Duflo, E., & Jackson, M.O. (2019) Using gossips to spread information: Theory and evidence from two randomized controlled trials. Review of Economic Studies 86, 2453–2490.Google Scholar
Beaman, L., BenYishay, A., Magruder, J., & Mobarak, A.M. (2021) Can network theory-based targeting increase technology adoption? American Economic Review 111(6), 1918–1943.CrossRefGoogle Scholar
Bramoullé, Y., Djebbari, H., & Fortin, B. (2009) Identification of peer effects through social networks. Journal of Econometrics 150, 41–55.CrossRefGoogle Scholar
Conley, T.G. & Udry, C.R. (2010) Learning about a new technology: Pineapple in Ghana. The American Economic Review 100(1), 35–69.Google Scholar
Dawid, P.A. (1979) Conditional Independence in statistical theory. Journal of the Royal Statistical Society. Series B 41, 1–31.Google Scholar
de Matos, M.G., Ferreira, P., & Krackhardt, D. (2014) Peer influence in the diffusion of the iPhone 3G over a large social network. Management Information System Quarterly 38, 1103–1133.CrossRefGoogle Scholar
de Paula, A., Rasul, I., & Souza, P.C. (2020) Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition. Preprint, arxiv:1910.07452v2.Google Scholar
Drukker, D.M., Egger, P.H., & Prucha, I.R. (2022) Simultaneous Equation Models with Higher Order Spatial or Social Network Interactions. Econometric Theory, in press.Google Scholar
Granovetter, M. (1978) Threshold models of collective behavior. American Journal of Sociology 83, 1420–1443.CrossRefGoogle Scholar
He, X. & Song, K. (2022) Measuring Diffusion over a Large Network. Preprint, arXiv:1812.04195v4 [stat.ME].Google Scholar
Hirano, K., Imbens, G., & Ridder, H. (2003) Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71, 1161–1189.CrossRefGoogle Scholar
Horowitz, J.L. (2009) Semiparametric and Nonparametric Methods in Econometrics . Springer.CrossRefGoogle Scholar
Imbens, G.W. & Wooldridge, J.M. (2009) Recent developments in the econometrics of program evaluation. Journal of Economic Literature 47, 5–86.CrossRefGoogle Scholar
Jackson, M.O. (2008) Social and Economic Networks . Princeton University Press.CrossRefGoogle Scholar
Jensen, D.R. (1984) Ordering ellipsoidal measures: Scale and peakness ordering. SIAM Journal on Applied Mathematics 44(6), 1226–1231.Google Scholar
Khan, S. & Tamer, E. (2010) Irregular identification, support conditions, and inverse weight estimation. Econometrica 78, 2021–2042.Google Scholar
Kojevnikov, D. (2021) The Bootstrap for Network Dependent Processes. Preprint, arXiv:2101.12312v1 [econ.EM].Google Scholar
Kojevnikov, D. & Song, K. (2022) Econometric Inference on Large Bayesian Games with Heterogeneous Beliefs. arXiv:1404.2015v3 [stat.AP].Google Scholar
Kuersteiner, G.M. & Prucha, I.R. (2013) Limit theory for panel data models with cross sectional dependence and sequential exogeneity. Journal of Econometrics 174(2), 107–126.Google ScholarPubMed
Lee, J.H. & Song, K. (2019) Stable limit theorems for empirical processes under conditional neighborhood dependence. Bernoulli 25, 1189–1224.CrossRefGoogle Scholar
Lehmann, E.L. & Romano, J.P. (2005) Testing Statistical Hypotheses . Springer.Google Scholar
Leskovec, J., Adamic, L.A., & Huberman, B.A. (2007) The dynamics of viral marketing. ACM Transactions on the Web 1(1), 5.CrossRefGoogle Scholar
Leung, M.P. (2020) Treatment and spillover effects under network interference. Review of Economics and Statistics 102, 368–380.CrossRefGoogle Scholar
Lewbel, A., Qu, X., & Tang, X. (2021) Social Networks with Unobserved Links. Working paper.Google Scholar
Manski, C.F. (1993) Identification of endogenous social effects: The reflection problem. Review of Economic Studies 60(3), 531–542.Google Scholar
Newman, M.E.J. (2010) Networks: An Introduction . Oxford University Press.Google Scholar
Penrose, M. (2003) Random Geometric Graphs . Oxford University Press.CrossRefGoogle Scholar
Phillips, P.C.B. (1988) Conditional and unconditional statistical Independence. Journal of Econometrics 38, 341–348.CrossRefGoogle Scholar
Romano, J.P. & Shaikh, A.M. (2010) Inference for the identified set in partially identified econometric models. Econometrica 78, 169–211.Google Scholar
Rosenbaum, P.R. & Rubin, D.B. (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1), 41–55.Google Scholar
Sadler, E. (2020) Diffusion games. American Economic Review 110, 225–270.CrossRefGoogle Scholar
Shalizi, C.R. & Thomas, A.C. (2011) Homophily and contagion are generically confounded in observational social network studies. Sociological Methods and Research 40, 211–239.CrossRefGoogle ScholarPubMed
Song, K. (2018) Measuring the graph concordance of locally dependent observations. Review of Economics and Statistics 100, 535–549.CrossRefGoogle Scholar
van der Laan, M. (2014) Causal inference for a population of causally connected units. Journal of Causal Inference 2, 13–74.Google ScholarPubMed