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High-Dosage Tutoring and Reading Achievement: Evidence from New York City

Author

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  • Roland G. Fryer, Jr
  • Meghan Howard Noveck

Abstract

This study examines the impact on student achievement of high-dosage reading tutoring for middle school students in New York City Public Schools, using a school-level randomized field experiment. Across three years, schools offered at least 130 hours of 4-on-1 tutoring based on a guided reading model, which consisted of 1-on-1 read alouds, independent reading, vocabulary review, and group discussion. We show that, at the mean, tutoring has a positive and significant effect on school attendance, a positive, but insignificant, effect on English Language Arts (ELA) state test scores and no effect on math state test scores. There is important heterogeneity by race. For black students, our treatment increased attendance by 2.0 percentage points (control mean 92.4 percent) and ELA scores by 0.09 standard deviations per year – two times larger than the effect of the Promise Academy Middle School in the Harlem Children’s Zone and KIPP Charter Middle Schools on reading achievement. For Hispanic students, the treatment effect is 0.8 percentage points on attendance (control mean 92.0 percent) and 0.01 standard deviations per year on ELA scores. We argue that the difference between the effectiveness of tutoring for black and Hispanic students is best explained by the average tutor characteristics at the schools they attend.

Suggested Citation

  • Roland G. Fryer, Jr & Meghan Howard Noveck, 2017. "High-Dosage Tutoring and Reading Achievement: Evidence from New York City," NBER Working Papers 23792, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:23792
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    References listed on IDEAS

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    1. Roland G. Fryer, Jr., 2014. "Injecting Charter School Best Practices into Traditional Public Schools: Evidence from Field Experiments," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(3), pages 1355-1407.
    2. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(3), pages 1071-1102.
    3. Steven Glazerman & Daniel Mayer & Paul Decker, 2006. "Alternative routes to teaching: The impacts of Teach for America on student achievement and other outcomes," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 25(1), pages 75-96.
    4. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    5. Caroline M. Hoxby & Sonali Murarka, 2009. "Charter Schools in New York City: Who Enrolls and How They Affect Their Students' Achievement," NBER Working Papers 14852, National Bureau of Economic Research, Inc.
    6. Roland G. Fryer, Jr, 2017. "Management and Student Achievement: Evidence from a Randomized Field Experiment," NBER Working Papers 23437, National Bureau of Economic Research, Inc.
    7. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
    8. Will Dobbie & Roland G. Fryer, 2011. "Are High-Quality Schools Enough to Increase Achievement among the Poor? Evidence from the Harlem Children's Zone," American Economic Journal: Applied Economics, American Economic Association, vol. 3(3), pages 158-187, July.
    9. Christina Clark Tuttle & Brian Gill & Philip Gleason & Virginia Knechtel & Ira Nichols-Barrer & Alexandra Resch, "undated". "KIPP Middle Schools: Impacts on Achievement and Other Outcomes (Executive Summary)," Mathematica Policy Research Reports ce0a1376d63744699ca19f917, Mathematica Policy Research.
    10. Joseph P. Romano & Azeem M. Shaikh & Michael Wolf, 2010. "Hypothesis Testing in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 75-104, September.
    11. Moulton, Brent R, 1990. "An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Unit," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 334-338, May.
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    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • J0 - Labor and Demographic Economics - - General

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