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Neighborhoods, Schools, and Adolescent Violence: Ecological Relative Deprivation, Disadvantage Saturation, or Cumulative Disadvantage?


Neighborhood and school socioeconomic “disadvantage” are consequential for youth violence perpetration. This study considers alternative ecological cumulative disadvantage, disadvantage saturation, and relative deprivation hypotheses regarding how the association between neighborhood disadvantage and violence varies by levels of socioeconomic disadvantage in schools. These hypotheses are tested with data from Wave I of Add Health (n = 15,581; 51% Female; Age mean = 15.67, SD = 1.74). Cross-classified multilevel Rasch models are used to estimate the interaction between neighborhood and school disadvantage in predicting adolescent violence. Consistent with the ecological relative deprivation hypothesis, results indicate that the association between neighborhood disadvantage and violence is most pronounced among youth attending low-disadvantage schools. Further, youth exposed to high-disadvantage neighborhoods and low-disadvantage schools tend to be at the greatest risk of perpetrating violence. These patterns are evident among both males and females, and particularly among older youth and those from low-parent education families. This study motivates future investigations considering how adolescents’ experiences beyond the neighborhood shape how they engage with and experience the effects of their neighborhoods.

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  1. While school FRPL has been shown to be an incomplete measure of school socioeconomic disadvantage, FRPL status nevertheless captures dimensions of educational disadvantage that other income-based indicators do not (Domina et al., 2018). Beyond income, school-level rates of parental education have also been shown to shape educational achievement and attainment (Crosnoe, 2009; Owens, 2010). Because the neighborhoods and violence literature tends to emphasize consequences of exposure to low-socioeconomic resources (Chang et al., 2016), aggregated reports of low-parent education and parental welfare receipt are used in combination with school FRPL reports to more fully capture school socioeconomic disadvantage.

  2. To understand the representativeness of the analytic sample with respect to school FRPL rates, Add Health school FRPL rates were compared with those reported by the NCES for the earliest available time period (1999-2000) (McFarland et al., 2019). The NCES reports the proportion of public school students attending “low poverty,” “mid-low poverty,” “mid-high poverty,” and “high poverty” categories of schools. Low poverty schools are those where < =25% of the student body is FRPL eligible. The cutoffs for mid-low poverty, mid-high poverty, and high poverty schools are 26-50%, 51-75%, and > =76%, respectively. In 1999-2000, 45% of public school students attended low poverty schools, 25% attended mid-low poverty schools, 16% attended mid-high poverty schools, and 12% attended high poverty schools. These categories were recreated using the Add Health FRPL data. In the present analytic sample weighted according to Add Health guidelines, 48.9% of students attend low poverty schools, 33.2% attend mid-low poverty schools, 12.6% attend mid-high poverty schools, and 5.3% attended high poverty schools. Thus, students attending “high poverty” schools are likely underrepresented in the current sample. This underrepresentation may bias the present results, but it is important to note that effects of nonresponse bias in multivariable models have been demonstrated be to minimal when controlling for design variables (Amaya & Presser, 2017; Rindfuss et al., 2015).

  3. In these analyses, the measures of racial composition and student population size are based on aggregations of available in-school and in-home Add Health survey responses when NCES data are missing.


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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 to 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 ( No direct support was received from grant P01-HD31921 for this analysis. Supplementary data come from the AHAA study, which was funded by a grant (R01 HD040428-02, Chandra Muller, PI) from the National Institute of Child Health and Human Development, and a grant (REC-0126167, Chandra Muller, PI, and Pedro Reyes, Co-PI) from the National Science Foundation, and received further support from grant 5 R24 HD042849, Population Research Center, awarded to the Population Research Center at The University of Texas at Austin by the Eunice Kennedy Shriver National Institute of Health and Child Development.

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N.P. conceived of the study, participated in its design, analyzed the data, and wrote the paper; R.S. conceived of the study, participated in its design, and wrote the paper. Both authors have read and approved the final version of this manuscript.


This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE1255832. This research was supported in part by a grant (R15HD070098-01A1) from the Eunice Kennedy Shriver National Institute of Child Health & Human Development, and by the Center for Family and Demographic Research, Bowling Green State University, which has core funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R24HD050959). The opinions and conclusions expressed herein are solely those of the author(s) and should not be construed as representing the opinions or policy of any agency of the Federal government.

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The manuscript’s data will not be deposited. See for more information about how to access Add Health data.

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Correspondence to Nicolo P. Pinchak.

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The authors declare no competing interests.

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This study complies with Add Health data access standards. See for more information about the Add Health access requirements and standards.

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Pinchak, N.P., Swisher, R.R. Neighborhoods, Schools, and Adolescent Violence: Ecological Relative Deprivation, Disadvantage Saturation, or Cumulative Disadvantage?. J Youth Adolescence 51, 261–277 (2022).

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  • Neighborhoods
  • Schools
  • Violence
  • Relative deprivation
  • Cumulative disadvantage
  • Activity space