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The Aftermath of Accelerating Algebra: Evidence from District Policy Initiatives

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  • Charles T. Clotfelter
  • Helen F. Ladd
  • Jacob L. Vigdor

Abstract

The proportion of students taking a first algebra course in middle school has doubled over the past generation and there have been calls to make eighth grade algebra universal. We use significant policy shifts in the timing of algebra in two large North Carolina districts to infer the impact of accelerated entry into algebra on student performance in math courses as students progress through high school. We find no evidence of a positive mean impact of acceleration in any specification and significant negative effects on performance in both Algebra I and the traditional followup course, Geometry. Accelerating algebra to middle school appears benign or beneficial for higherperforming students but unambiguously harmful to the lowest performers. We consider whether the effects reflect the reliance on less-qualified teachers and conclude that this mechanism explains only a small fraction of the result.

Suggested Citation

  • Charles T. Clotfelter & Helen F. Ladd & Jacob L. Vigdor, 2015. "The Aftermath of Accelerating Algebra: Evidence from District Policy Initiatives," Journal of Human Resources, University of Wisconsin Press, vol. 50(1), pages 159-188.
    • Handle: RePEc:uwp:jhriss:v:50:y:2015:i:1:p:159-188
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    Cited by:

    1. Thurston Domina & Andrew McEachin & Paul Hanselman & Priyanka Agarwal & NaYoung Hwang & Ryan Lewis, 2016. "Beyond Tracking and Detracking The Dimensions of Organizational Differentiation in Schools," Working Papers WR-1155, RAND Corporation.
    2. Huebener, Mathias & Marcus, Jan, 2017. "Compressing instruction time into fewer years of schooling and the impact on student performance," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 58, pages 1-14.
    3. Han Yu & Naci Mocan, 2018. "The Impact of High School Curriculum on Confidence, Academic Success, and Mental and Physical Well-Being of University Students," NBER Working Papers 24573, National Bureau of Economic Research, Inc.
    4. Charles T. Clotfelter & Steven W. Hemelt & Helen F. Ladd, 2019. "Raising the Bar for College Admission: North Carolina's Increase in Minimum Math Course Requirements," Education Finance and Policy, MIT Press, vol. 14(3), pages 492-521, Summer.
    5. Ainoa Aparicio Fenoll & Sarah Zaccagni, 2021. "Gender Mix and Team Performance: Differences between Exogenously and Endogenously Formed Teams," Carlo Alberto Notebooks 646, Collegio Carlo Alberto.
    6. Cory Koedel & Mark Ehlert & Eric Parsons & Michael Podgursky, 2012. "Selecting Growth Measures for School and Teacher Evaluations," Working Papers 1210, Department of Economics, University of Missouri.
    7. Huebener, Mathias & Kuger, Susanne & Marcus, Jan, 2017. "Increased instruction hours and the widening gap in student performance," Labour Economics, Elsevier, vol. 47(C), pages 15-34.
    8. Dahmann, Sarah C., 2017. "How does education improve cognitive skills? Instructional time versus timing of instruction," Labour Economics, Elsevier, vol. 47(C), pages 35-47.
    9. Jia, Ning, 2021. "Do stricter high school math requirements raise college STEM attainment?," Economics of Education Review, Elsevier, vol. 83(C).
    10. Vincenzo Andrietti & Xuejuan Su, 2019. "The Impact of Schooling Intensity on Student Learning: Evidence from a Quasi-Experiment," Education Finance and Policy, MIT Press, vol. 14(4), pages 679-701, Fall.
    11. Heissel, Jennifer, 2016. "The relative benefits of live versus online delivery: Evidence from virtual algebra I in North Carolina," Economics of Education Review, Elsevier, vol. 53(C), pages 99-115.
    12. Andrew McEachin & Thurston Domina & Andrew Penner, 2020. "Heterogeneous Effects of Early Algebra across California Middle Schools," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(3), pages 772-800, June.
    13. Joshua Goodman & Dougherty, Shaun & Darryl Hill & Erica Litke & Lindsay Page, 2015. "Early Math Coursework and College Readiness: Evidence from Targeted Middle School Math Acceleration," Working Paper 283481, Harvard University OpenScholar.
    14. Fenoll, Ainoa Aparicio & Moscarola, Flavia Coda & Zaccagni, Sarah, 2021. "Mathematics camps: A gift for gifted students?," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 738-751.
    15. Dougherty, Shaun M. & Goodman, Joshua S. & Hill, Darryl V. & Litke, Erica G. & Page, Lindsay C., 2017. "Objective course placement and college readiness: Evidence from targeted middle school math acceleration," Economics of Education Review, Elsevier, vol. 58(C), pages 141-161.
    16. Kalena E. Cortes & Joshua S. Goodman & Takako Nomi, 2015. "Intensive Math Instruction and Educational Attainment: Long-Run Impacts of Double-Dose Algebra," Journal of Human Resources, University of Wisconsin Press, vol. 50(1), pages 108-158.
    17. Cortes, Kalena E. & Goodman, Joshua Samuel & Nomi, Takako, 2015. "Intensive Math Instruction and Educational Attainment," Scholarly Articles 34298862, Harvard Kennedy School of Government.
    18. repec:umc:wpaper:1308 is not listed on IDEAS
    19. Ainoa Aparicio Fenoll & Flavia Coda-Moscarola & Sarah Zaccagni, 2021. "Mathematics Camps: A Gift for Gifted Students," Carlo Alberto Notebooks 647, Collegio Carlo Alberto.
    20. Arce-Trigatti, Paula, 2018. "The impact of state-mandated Advanced Placement programs on student outcomes," Economics of Education Review, Elsevier, vol. 63(C), pages 180-193.
    21. Kurtz, Michael D. & Conway, Karen Smith & Mohr, Robert D., 2020. "Weekend feeding (“BackPack”) programs and student outcomes," Economics of Education Review, Elsevier, vol. 79(C).
    22. Eric Parsons, 2014. "Does Attending a Low-Achieving School Affect High-Performing Student Outcomes?," Working Papers 1407, Department of Economics, University of Missouri, revised 18 Feb 2015.
    23. Lars J. Kirkebøen & Trude Gunnes & Lena Lindenskov & Marte Rønning, 2021. "Didactic methods and small-group instruction for low-performing adolescents in mathematics. Results from a randomized controlled trial," Discussion Papers 957, Statistics Norway, Research Department.

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    More about this item

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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