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The impact of homework on student achievement

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  • Ozkan Eren
  • Daniel J. Henderson

Abstract

Utilizing parametric and nonparametric techniques, we assess the role of a heretofore relatively unexplored "input" in the educational process, homework, on academic achievement. Our results indicate that homework is an important determinant of student test scores. Relative to more standard spending related measures, extra homework has a larger and more significant impact on test scores. However, the effects are not uniform across different subpopulations. Specifically, we find additional homework to be most effective for high and low achievers, which is further confirmed by stochastic dominance analysis. Moreover, the parametric estimates of the educational production function overstate the impact of schooling related inputs. In all estimates, the homework coefficient from the parametric model maps to the upper deciles of the nonparametric coefficient distribution and as a by-product the parametric model understates the percentage of students with negative responses to additional homework. Copyright © 2008 The Author(s). Journal compilation © Royal Economic Society 2008

Suggested Citation

  • Ozkan Eren & Daniel J. Henderson, 2008. "The impact of homework on student achievement," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 326-348, July.
  • Handle: RePEc:ect:emjrnl:v:11:y:2008:i:2:p:326-348
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    Cited by:

    1. Darren Grant & William Green, 2013. "Grades as incentives," Empirical Economics, Springer, vol. 44(3), pages 1563-1592, June.
    2. Bettina Büttner & Stephan L. Thomsen, 2015. "Are We Spending Too Many Years in School? Causal Evidence of the Impact of Shortening Secondary School Duration," German Economic Review, Verein für Socialpolitik, vol. 16(1), pages 65-86, February.
    3. Nicolas Grau, 2013. "The Impact of College Admissions Policies on The Performance of High School Students," PIER Working Paper Archive 13-040, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    4. Deniz Ozabaci & Daniel Henderson, 2015. "Additive kernel estimates of returns to schooling," Empirical Economics, Springer, vol. 48(1), pages 227-251, February.
    5. Deniz Baglan & Hakan Yilmazkuday, 2018. "Financial Health and the Intensive Margin of Trade," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(6), pages 1304-1319, May.
    6. Han Yu & Naci Mocan, 2019. "The Impact of High School Curriculum on Confidence, Academic Success, and Mental and Physical Well-Being of University Students," Journal of Labor Research, Springer, vol. 40(4), pages 428-462, December.
    7. Zhu, Rong, 2011. "NILS Working paper no 170. The impact of major--job mismatch on college graduates' early career earnings," NILS Working Papers 26072, National Institute of Labour Studies.
    8. Valentin Zelenyuk & Leopold Simar, 2011. "To Smooth or Not to Smooth? The Case of Discrete Variables in Nonparametric Regressions," CEPA Working Papers Series WP102011, School of Economics, University of Queensland, Australia.
    9. Steven McMullen, 2011. "How do Students Respond to Labor Market and Education Incentives? An Analysis of Homework Time," Journal of Labor Research, Springer, vol. 32(3), pages 199-209, September.
    10. Rong Zhu, 2014. "The impact of major-job mismatch on college graduates' early career earnings: evidence from China," Education Economics, Taylor & Francis Journals, vol. 22(5), pages 511-528, October.
    11. Torberg Falch & Marte Rønning, 2011. "Homework assignment and student achievement in OECD countries," Working Paper Series 11411, Department of Economics, Norwegian University of Science and Technology.
    12. Eren, Ozkan & Henderson, Daniel J., 2011. "Are we wasting our children's time by giving them more homework?," Economics of Education Review, Elsevier, vol. 30(5), pages 950-961, October.
    13. Daniel J. Henderson & Andrew Houtenville & Le Wang, 2017. "The Distribution of Returns to Education for People with Disabilities," Journal of Labor Research, Springer, vol. 38(3), pages 261-282, September.
    14. Carlos Cortinhas, 2017. "Does formative feedback help or hinder students? An empirical investigation," Discussion Papers 1701, University of Exeter, Department of Economics.
    15. Daniel J. Henderson, 2010. "A test for multimodality of regression derivatives with application to nonparametric growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(3), pages 458-480.
    16. Li, Degui & Simar, Léopold & Zelenyuk, Valentin, 2016. "Generalized nonparametric smoothing with mixed discrete and continuous data," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 424-444.
    17. Henderson, Daniel J. & Maasoumi, Esfandiar, 2012. "Searching for Rehabilitation in Nonparametric Regression Models with Exogenous Treatment Assignment," IZA Discussion Papers 6874, Institute of Labor Economics (IZA).
    18. Grau, Nicolás, 2018. "The impact of college admissions policies on the academic effort of high school students," Economics of Education Review, Elsevier, vol. 65(C), pages 58-92.
    19. Kalenkoski, Charlene Marie & Pabilonia, Sabrina Wulff, 2012. "Time to work or time to play: The effect of student employment on homework, sleep, and screen time," Labour Economics, Elsevier, vol. 19(2), pages 211-221.
    20. José Antonio Molina Marfil & Oscar David Marcenaro Gutierrez & Ana Martín Marcos, 2016. "Procesos de enseñanza-aprendizaje y producción educativa: un análisis de la competencia matemática," Investigaciones de Economía de la Educación volume 11, in: José Manuel Cordero Ferrera & Rosa Simancas Rodríguez (ed.), Investigaciones de Economía de la Educación 11, edition 1, volume 11, chapter 32, pages 585-604, Asociación de Economía de la Educación.
    21. Christopher Clark & Benjamin Scafidi & John R. Swinton, 2012. "Does Ap Economics Improve Student Achievement?," The American Economist, Sage Publications, vol. 57(1), pages 1-20, May.

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

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