Understanding neighborhood disadvantage: A behavior genetic analysis

Albert J. Ksinan; Alexander T. Vazsonyi

doi:10.1016/j.jcrimjus.2021.101782

Understanding neighborhood disadvantage: A behavior genetic analysis

Research output: Contribution to journal › Article › peer-review

Abstract

Purpose: Studies have shown that disadvantaged neighborhoods are associated with higher levels of crime and delinquent behaviors. Existing explanations do not adequately address how individuals select neighborhood. Thus, the current study employed a genetically-informed design to test whether living in a disadvantaged neighborhood might be partly explained by individual characteristics, including self-control and cognitive ability. Method: A sibling subsample of N = 1573 Add Health siblings living away from their parents at Wave 4 was used in twin analyses to assess genetic and environmental effects on neighborhood disadvantage. To evaluate which individual-level variables might longitudinally predict neighborhood disadvantage, a sample of N = 12,405 individuals was used. Results: Findings provided evidence of significant heritability (32%) of neighborhood disadvantage. In addition, a significant negative effect by adolescent cognitive ability on neighborhood disadvantage 14 years later was observed (β = −0.04, p =. 002). Follow-up analyses showed a genetic effect on the association between cognitive ability and neighborhood disadvantage. Conclusions: Study findings indicate substantial heritability of neighborhood disadvantage, showing that individuals themselves might potentially contribute to a self-selection process that explains which neighborhoods they occupy as adults.

Original language	English
Article number	101782
Journal	Journal of Criminal Justice
Volume	73
DOIs	https://doi.org/10.1016/j.jcrimjus.2021.101782
State	Published - Mar 1 2021

Bibliographical note

Funding Information:
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 the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.

Funding Information:
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 the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website ( http://www.cpc.unc.edu/addhealth ). No direct support was received from grant P01-HD31921 for this analysis.

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

Adolescence
Behavior genetics
Longitudinal analysis
Neighborhood disadvantage

ASJC Scopus subject areas

Social Psychology
Applied Psychology
Sociology and Political Science
Law

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.jcrimjus.2021.101782

Cite this

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title = "Understanding neighborhood disadvantage: A behavior genetic analysis",

abstract = "Purpose: Studies have shown that disadvantaged neighborhoods are associated with higher levels of crime and delinquent behaviors. Existing explanations do not adequately address how individuals select neighborhood. Thus, the current study employed a genetically-informed design to test whether living in a disadvantaged neighborhood might be partly explained by individual characteristics, including self-control and cognitive ability. Method: A sibling subsample of N = 1573 Add Health siblings living away from their parents at Wave 4 was used in twin analyses to assess genetic and environmental effects on neighborhood disadvantage. To evaluate which individual-level variables might longitudinally predict neighborhood disadvantage, a sample of N = 12,405 individuals was used. Results: Findings provided evidence of significant heritability (32%) of neighborhood disadvantage. In addition, a significant negative effect by adolescent cognitive ability on neighborhood disadvantage 14 years later was observed (β = −0.04, p =. 002). Follow-up analyses showed a genetic effect on the association between cognitive ability and neighborhood disadvantage. Conclusions: Study findings indicate substantial heritability of neighborhood disadvantage, showing that individuals themselves might potentially contribute to a self-selection process that explains which neighborhoods they occupy as adults.",

keywords = "Adolescence, Behavior genetics, Longitudinal analysis, Neighborhood disadvantage",

author = "Ksinan, {Albert J.} and Vazsonyi, {Alexander T.}",

note = "Funding Information: 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 the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis. Funding Information: 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 the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website ( http://www.cpc.unc.edu/addhealth ). No direct support was received from grant P01-HD31921 for this analysis. Publisher Copyright: {\textcopyright} 2021 Elsevier Ltd",

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AU - Vazsonyi, Alexander T.

N1 - Funding Information: 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 the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis. Funding Information: 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 the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website ( http://www.cpc.unc.edu/addhealth ). No direct support was received from grant P01-HD31921 for this analysis. Publisher Copyright: © 2021 Elsevier Ltd

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N2 - Purpose: Studies have shown that disadvantaged neighborhoods are associated with higher levels of crime and delinquent behaviors. Existing explanations do not adequately address how individuals select neighborhood. Thus, the current study employed a genetically-informed design to test whether living in a disadvantaged neighborhood might be partly explained by individual characteristics, including self-control and cognitive ability. Method: A sibling subsample of N = 1573 Add Health siblings living away from their parents at Wave 4 was used in twin analyses to assess genetic and environmental effects on neighborhood disadvantage. To evaluate which individual-level variables might longitudinally predict neighborhood disadvantage, a sample of N = 12,405 individuals was used. Results: Findings provided evidence of significant heritability (32%) of neighborhood disadvantage. In addition, a significant negative effect by adolescent cognitive ability on neighborhood disadvantage 14 years later was observed (β = −0.04, p =. 002). Follow-up analyses showed a genetic effect on the association between cognitive ability and neighborhood disadvantage. Conclusions: Study findings indicate substantial heritability of neighborhood disadvantage, showing that individuals themselves might potentially contribute to a self-selection process that explains which neighborhoods they occupy as adults.

AB - Purpose: Studies have shown that disadvantaged neighborhoods are associated with higher levels of crime and delinquent behaviors. Existing explanations do not adequately address how individuals select neighborhood. Thus, the current study employed a genetically-informed design to test whether living in a disadvantaged neighborhood might be partly explained by individual characteristics, including self-control and cognitive ability. Method: A sibling subsample of N = 1573 Add Health siblings living away from their parents at Wave 4 was used in twin analyses to assess genetic and environmental effects on neighborhood disadvantage. To evaluate which individual-level variables might longitudinally predict neighborhood disadvantage, a sample of N = 12,405 individuals was used. Results: Findings provided evidence of significant heritability (32%) of neighborhood disadvantage. In addition, a significant negative effect by adolescent cognitive ability on neighborhood disadvantage 14 years later was observed (β = −0.04, p =. 002). Follow-up analyses showed a genetic effect on the association between cognitive ability and neighborhood disadvantage. Conclusions: Study findings indicate substantial heritability of neighborhood disadvantage, showing that individuals themselves might potentially contribute to a self-selection process that explains which neighborhoods they occupy as adults.

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KW - Longitudinal analysis

KW - Neighborhood disadvantage

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Understanding neighborhood disadvantage: A behavior genetic analysis

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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