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Estimating Peer Effects on Career Choice: A Spatial Multinomial Logit Approach

In: Essays in Honor of Cheng Hsiao

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  • Bolun Li
  • Robin Sickles
  • Jenny Williams

Abstract

Peers and friends are among the most influential social forces affecting adolescent behavior. In this chapter, the authors investigate peer effects on post high school career decisions and on school choice. The authors define peers as students who are in the same classes and social clubs and measure peer effects as spatial dependence among them. Utilizing recent developments in spatial econometrics, the authors formalize a spatial multinomial choice model in which individuals are spatially dependent in their preferences. The authors estimate the model via pseudo maximum likelihood using data from the Texas Higher Education Opportunity Project. The authors do find that individuals are positively correlated in their career and college preferences and examine how such dependencies impact decisions directly and indirectly as peer effects are allowed to reverberate through the social network in which students reside.

Suggested Citation

  • Bolun Li & Robin Sickles & Jenny Williams, 2020. "Estimating Peer Effects on Career Choice: A Spatial Multinomial Logit Approach," Advances in Econometrics, in: Essays in Honor of Cheng Hsiao, volume 41, pages 359-381, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320200000041013
    DOI: 10.1108/S0731-905320200000041013
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    References listed on IDEAS

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    1. David J. Zimmerman, 2003. "Peer Effects in Academic Outcomes: Evidence from a Natural Experiment," The Review of Economics and Statistics, MIT Press, vol. 85(1), pages 9-23, February.
    2. Weili Ding & Steven F. Lehrer, 2007. "Do Peers Affect Student Achievement in China's Secondary Schools?," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 300-312, May.
    3. Smirnov, Oleg A. & Egan, Kevin J., 2012. "Spatial random utility model with an application to recreation demand," Economic Modelling, Elsevier, vol. 29(1), pages 72-78.
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    5. Arcidiacono, Peter & Nicholson, Sean, 2005. "Peer effects in medical school," Journal of Public Economics, Elsevier, vol. 89(2-3), pages 327-350, February.
    6. Kelejian, Harry H. & Piras, Gianfranco, 2014. "Estimation of spatial models with endogenous weighting matrices, and an application to a demand model for cigarettes," Regional Science and Urban Economics, Elsevier, vol. 46(C), pages 140-149.
    7. Smirnov, Oleg A., 2010. "Modeling spatial discrete choice," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 292-298, September.
    8. James P. LeSage, 2014. "What Regional Scientists Need to Know about Spatial Econometrics," The Review of Regional Studies, Southern Regional Science Association, vol. 44(1), pages 13-32, Spring.
    9. Matthew Neidell & Jane Waldfogel, 2010. "Cognitive and Noncognitive Peer Effects in Early Education," The Review of Economics and Statistics, MIT Press, vol. 92(3), pages 562-576, August.
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    More about this item

    Keywords

    Spatial models; peer effect; spatial random utility; multinomial logit models; school attendance; career choice; JEL Classification; C31; C35;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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