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Identifying peer effects using spatial analysis: the role of peers on risky sexual behavior

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Abstract

This paper explores the role of peer effects on early sexual debut for a sample of adolescents using data from the National Longitudinal Study on Adolescent Health (Add Health). Most studies analyzing peer influences ignore the “reflection” problem that occurs with studying peer effects. To address the reflection problem, this paper employs a spatial econometric approach to estimate a social interactions model. This is the first study in the literature on adolescent risky sexual behavior to use this approach to estimate peer effects. Similar to other research on peer effects and adolescent risky sexual behavior, this paper finds the existence of peer effects. However, the more vital outcome from this study is that older and male peers increase the likelihood of adolescent early sexual debut, while peers whose mothers are more open about sexual activity decrease adolescent risky sexual behavior. This methodology can help further our knowledge about the social context that influences adolescent sexual behavior.

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Notes

  1. Cliff and Ord (1981).

  2. “The In-School Questionnaire yields full social network data for most students in 140 schools. Students were asked to identify up to five male and five female friends, to locate and record their student numbers, and to indicate which of five activities they had done with each of these friends during the past week. Because friends’ student numbers were recorded, friendship networks can be determined and a respondent’s peer group, as well as his or her position within it, can be described in detail. Multiple measures of the strength of friendship ties are available. Patterns of association within the school community, the density and centralization of the social network, and the degree to which it is fractured on lines of race, gender, or behaviors can be computed. In-home interviews of adolescents in the saturation sample (i.e. adolescents who attended schools in which all students were solicited for in-home interviews) elicited nominations of the five closest opposite-sex and five closest same sex friends who, it is likely, were also interviewed. The remainder of the in-home sample was asked about only one male and one female friend”. (Source: http://www.cpc.unc.edu/projects/addhealth/design/contexts).

  3. However, Averett and Estelle (2013) find estimates on parental communication tend to be overstated.

  4. Programs to estimate the models in MATLAB were provided through James LeSage’s Spatial Econometrics toolbox. The specific programs to carry out the MCMC estimation (sarpx_g.m) and calculate the confidence intervals (cr_interval.m) were downloaded from the Journal of the Royal Statistical Society: Series A website, available at the following link: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-985X/homepage/174_4.htm.

  5. See Lacombe and LeSage (2013).

  6. In the case where λ is zero, the model collapses into a conventional probit model. For the SDM, the marginal effects would be Inβi + Wθi.

  7. The calculation of these effects is applicable for the spatial durbin probit models (see footnote 4 for a link to the code).

  8. Friends who attended different schools were omitted from the sample.

  9. Full results are available upon request from the author.

  10. The reported marginal effects are just the partial derivatives calculated for probit models in STATA using the command dprobit, since X and WX are both exogenous explanatory variables (see footnote 6).

  11. I also estimated models for condom use and different specifications of sex with multiple partners.

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Acknowledgments

The author would like to thank the editor and two anonymous referees for comments and helpful suggestions. The author would also like to thank seminar participants at the Second Wave Conference at the Ohio State University and the 2012 Add Health User Conference for comments on an earlier draft. 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.

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  1. Department of Economics, University of Toledo, Toledo, OH, USA

    Olugbenga Ajilore

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Ajilore, O. Identifying peer effects using spatial analysis: the role of peers on risky sexual behavior. Rev Econ Household 13, 635–652 (2015). https://doi.org/10.1007/s11150-013-9235-4

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