Who is the key player? A network analysis of juvenile delinquency
Who is the key player? A network analysis of juvenile delinquency
This article presents a methodology for empirically identifying the key player, whose removal from the network leads to the optimal change in aggregate activity level in equilibrium [Ballester, C., Calvó-Armengol, A., and Zenou, Y. (2006), “Who’s Who in Networks. Wanted: The Key Player,” Econometrica, 74: 1403–1417], allowing the network links to rewire after the removal of the key player. First, we propose an IV-based estimation strategy for the social-interaction effect, which is needed to determine the equilibrium activity level of a network, taking into account the potential network endogeneity. Next, to simulate the network evolution process after the removal of the key player, we adopt the general network formation model in Mele [(2017), “A Structural Model of Dense Network Formation,” Econometrica, 85: 825–850] and extend it to incorporate the unobserved individual heterogeneity in link formation decisions. We illustrate the methodology by providing the key player rankings in juvenile delinquency using information on friendship networks among U.S. teenagers. We find that the key player is not necessarily the most active delinquent or the delinquent who ranks the highest in standard (not microfounded) centrality measures. We also find that, compared to a policy that removes the most active delinquent from the network, a key-player-targeted policy leads to a much higher delinquency reduction.
Key player policies, Linear social interaction models, Network centrality measures, Network endogeneity, Network formation
Lee, Lung-Fei
d672e819-8e46-4cd0-815a-35045b51254f
Liu, Xiaodong
8a273952-e4cf-4345-b45c-072ba5ba74c3
Patacchini, Eleonora
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Zenou, Yves
38bf0c72-462b-4c08-8fd1-ce365b0296dc
Lee, Lung-Fei
d672e819-8e46-4cd0-815a-35045b51254f
Liu, Xiaodong
8a273952-e4cf-4345-b45c-072ba5ba74c3
Patacchini, Eleonora
42a2cbc9-016c-43f2-a9e9-e2f00172d919
Zenou, Yves
38bf0c72-462b-4c08-8fd1-ce365b0296dc
Lee, Lung-Fei, Liu, Xiaodong, Patacchini, Eleonora and Zenou, Yves
(2020)
Who is the key player? A network analysis of juvenile delinquency.
Journal of Business and Economic Statistics.
(doi:10.1080/07350015.2020.1737082).
Abstract
This article presents a methodology for empirically identifying the key player, whose removal from the network leads to the optimal change in aggregate activity level in equilibrium [Ballester, C., Calvó-Armengol, A., and Zenou, Y. (2006), “Who’s Who in Networks. Wanted: The Key Player,” Econometrica, 74: 1403–1417], allowing the network links to rewire after the removal of the key player. First, we propose an IV-based estimation strategy for the social-interaction effect, which is needed to determine the equilibrium activity level of a network, taking into account the potential network endogeneity. Next, to simulate the network evolution process after the removal of the key player, we adopt the general network formation model in Mele [(2017), “A Structural Model of Dense Network Formation,” Econometrica, 85: 825–850] and extend it to incorporate the unobserved individual heterogeneity in link formation decisions. We illustrate the methodology by providing the key player rankings in juvenile delinquency using information on friendship networks among U.S. teenagers. We find that the key player is not necessarily the most active delinquent or the delinquent who ranks the highest in standard (not microfounded) centrality measures. We also find that, compared to a policy that removes the most active delinquent from the network, a key-player-targeted policy leads to a much higher delinquency reduction.
Text
JBES-P-2018-400-Paper
- Accepted Manuscript
More information
Accepted/In Press date: 27 February 2020
e-pub ahead of print date: 1 April 2020
Additional Information:
Funding Information:
A previous version of this article was circulated under the title: “Criminal Networks: Who is the Key Player?”. We thank the editor, Christian Hansen, an associate editor as well as two anonymous referees for very helpful comments. This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01 HD31921 from Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), with cooperative funding from 23 other federal agencies and foundations. Add Health GWAS data were funded by NICHD Grants R01 HD073342 (Harris) and R01 HD060726 (Harris, Boardman, and McQueen). Investigators thank the staff and participants of the Add Health Study for their important contributions. No direct support was received from grants P01 HD31921, R01 HD073342 and R01 HD060726 for this research.
Publisher Copyright:
© 2020, © 2020 American Statistical Association.
Keywords:
Key player policies, Linear social interaction models, Network centrality measures, Network endogeneity, Network formation
Identifiers
Local EPrints ID: 438415
URI: http://eprints.soton.ac.uk/id/eprint/438415
ISSN: 0735-0015
PURE UUID: b8b1b4e5-b17c-44d8-ba4b-27ec1b6d41e6
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Date deposited: 09 Mar 2020 17:33
Last modified: 17 Mar 2024 05:23
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Contributors
Author:
Lung-Fei Lee
Author:
Xiaodong Liu
Author:
Eleonora Patacchini
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