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Inside the 'Black Box' of Project STAR: Estimation of Peer Effects Using Experimental Data

Author

Listed:
  • Michael A. Boozer

    (Economic Growth Center, Yale University)

  • Stephen E. Cacciola

Abstract

The credible identification of endogenous peer group effects -- i.e. social multiplier or feedback effects -- has long eluded social scientists. We argue that such effects are most credibly identified by a randomly assigned social program which operates at differing intensities within and between peer groups. The data we use are from Project STAR, a class size reduction experiment conducted in Tennessee elementary schools. In these data, classes were comprised of varying fractions of students who had previously been exposed to the Small class treatment, creating class groupings of varying experimentally induced quality. We use this variation in class group quality to estimate the spillover effect. We find that when allowance is made for this 'feedback' effect of prior exposure to the Small class treatment, the peer effects account for much of the total experimental effects in the later grades, and the direct class size effects are rendered substantially smaller.

Suggested Citation

  • Michael A. Boozer & Stephen E. Cacciola, 2001. "Inside the 'Black Box' of Project STAR: Estimation of Peer Effects Using Experimental Data," Working Papers 832, Economic Growth Center, Yale University.
  • Handle: RePEc:egc:wpaper:832
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    File URL: http://www.econ.yale.edu/growth_pdf/cdp832.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Peer Effects; Data with a Group Structure; Organization of Schooling; Experimental Evidence;
    All these keywords.

    JEL classification:

    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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