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The Effects of Remedial Mathematics on the Learning of Economics: Evidence from a Natural Experiment

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  • Johan N. M. Lagerlöf
  • Andrew J. Seltzer

Abstract

The authors examined the effects of remedial mathematics on performance in university-level economics courses using a natural experiment. They studied exam results prior and subsequent to the implementation of a remedial mathematics course that was compulsory for a subset of students and unavailable for the others, controlling for background variables. They found that, consistent with previous studies, the level of and performance in secondary school mathematics have strong predictive power on students' performances at university-level economics. However, they found relatively little evidence for a positive effect of remedial mathematics on student performance.

Suggested Citation

  • Johan N. M. Lagerlöf & Andrew J. Seltzer, 2009. "The Effects of Remedial Mathematics on the Learning of Economics: Evidence from a Natural Experiment," The Journal of Economic Education, Taylor & Francis Journals, vol. 40(2), pages 115-137, April.
    • Handle: RePEc:taf:jeduce:v:40:y:2009:i:2:p:115-137
    DOI: 10.3200/JECE.40.2.115-137
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    1. J. S. Butler & T. Aldrich Finegan & John J. Siegfried, 1998. "Does more calculus improve student learning in intermediate micro- and macroeconomic theory?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(2), pages 185-202.
    2. Eric P. Bettinger & Bridget Terry Long, 2009. "Addressing the Needs of Underprepared Students in Higher Education: Does College Remediation Work?," Journal of Human Resources, University of Wisconsin Press, vol. 44(3).
    3. Eide, Eric & Showalter, Mark H., 1998. "The effect of school quality on student performance: A quantile regression approach," Economics Letters, Elsevier, vol. 58(3), pages 345-350, March.
    4. Dolton, Peter J & Makepeace, G H, 1990. "The Earnings of Economics Graduates," Economic Journal, Royal Economic Society, vol. 100(399), pages 237-250, March.
    5. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    6. Massimiliano BRATTI & Luca MANCINI, 2003. "Differences in Early Occupational Earnings of UK Male Graduates by Degree Subject: Evidence from the 1980-1993 USR," Working Papers 189, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    7. Charles L. Ballard & Marianne F. Johnson, 2004. "Basic Math Skills and Performance in an Introductory Economics Class," The Journal of Economic Education, Taylor & Francis Journals, vol. 35(1), pages 3-23, January.
    8. Jane S. Lopus, 1997. "Effects of the High School Economics Curriculum on Learning in the College Principles Class," The Journal of Economic Education, Taylor & Francis Journals, vol. 28(2), pages 143-153, June.
    9. Dolado, Juan J & Morales, Eduardo, 2007. "Which Factors Determine Academic Performance of Undergraduate Students in Economics? Some Spanish Evidence," CEPR Discussion Papers 6237, C.E.P.R. Discussion Papers.
    10. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    11. Durden, Garey C & Ellis, Larry V, 1995. "The Effects of Attendance on Student Learning in Principles of Economics," American Economic Review, American Economic Association, vol. 85(2), pages 343-346, May.
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    Cited by:

    1. Melanie A. Fennell & Irene R. Foster, 2021. "Test Format and Calculator Use in the Testing of Basic Math Skills for Principles of Economics: Experimental Evidence," The American Economist, Sage Publications, vol. 66(1), pages 29-45, March.
    2. Büchele, Stefan, 2020. "Should we trust math preparatory courses? An empirical analysis on the impact of students’ participation and attendance on short- and medium-term effects," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 154-167.
    3. Leiv Opstad, 2023. "The Relationship Between Norwegian Business Students’ Attitudes Towards Mathematics And Success In Business Education," International Journal of Teaching and Education, European Research Center, vol. 11(1), pages 47-60, December.
    4. Dino Alves & Ana Balcao Reis & Carmo Seabra & Luis Catela-Nunes, 2015. "Determinants of Academic Success in Economics and Management," Investigaciones de Economía de la Educación volume 10, in: Marta Rahona López & Jennifer Graves (ed.), Investigaciones de Economía de la Educación 10, edition 1, volume 10, chapter 17, pages 335-356, Asociación de Economía de la Educación.
    5. Stefan Buechele, 2019. "Should We Trust Math Preparatory Courses? An Empirical Analysis on the Impact of Students' Participation and Attendance on Short- and Medium-Term Effects," MAGKS Papers on Economics 201927, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    6. Carlos Arias & Javier Valbuena & Jose Manuel Garcia, 2021. "The Impact of Secondary Education Choices on Mathematical Performance in University: The Role of Non-Cognitive Skills," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    7. Carlos J. Asarta & Roger B. Butters & Andrew Perumal, 2013. "Success in Economics Major: Is it Path Dependent?," Working Papers 13-11, University of Delaware, Department of Economics.
    8. Ann L. Owen, 2011. "Student Characteristics, Behavior, and Performance in Economics Classes," Chapters, in: Gail M. Hoyt & KimMarie McGoldrick (ed.), International Handbook on Teaching and Learning Economics, chapter 32, Edward Elgar Publishing.
    9. Elena Moreno-García & Arturo García-Santillán & Némesis Larracilla Salazar & Milka Elena Escalera-Chávez, 2019. "Anxiety about Mathematics among Economics Students in Mexico," Mathematics, MDPI, vol. 7(5), pages 1-12, May.
    10. Girijasankar Mallik & John Lodewijks, 2010. "Student Performance in a Large First Year Economics Subject: Which Variables are Significant?," Economic Papers, The Economic Society of Australia, vol. 29(1), pages 80-86, March.
    11. Ivo J. M. Arnold & Wietske Rowaan, 2014. "First-Year Study Success in Economics and Econometrics: The Role of Gender, Motivation, and Math Skills," The Journal of Economic Education, Taylor & Francis Journals, vol. 45(1), pages 25-35, March.
    12. Maria Paola & Vincenzo Scoppa, 2014. "The effectiveness of remedial courses in Italy: a fuzzy regression discontinuity design," Journal of Population Economics, Springer;European Society for Population Economics, vol. 27(2), pages 365-386, April.
    13. Stefan Buechele, 2018. "Bridging the Gap - how Effective are Remedial Math Courses in Germany?," MAGKS Papers on Economics 201825, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    14. Juan J. Dolado & Eduardo Morales, 2009. "Which factors determine academic performance of Economics freshers? Some Spanish evidence," Investigaciones Economicas, Fundación SEPI, vol. 33(2), pages 179-210, May.
    15. Tasnádi, Attila & Kánnai, Zoltán & Pintér, Miklós, 2010. "Matematikaoktatás a bolognai típusú gazdasági képzésekben [Maths instruction in Bologna-type economics tuition]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(3), pages 261-277.
    16. Ivo J. M. Arnold & Jerry T. Straten, 2012. "Motivation and Math Skills as Determinants of First-Year Performance in Economics," The Journal of Economic Education, Taylor & Francis Journals, vol. 43(1), pages 33-47, January.

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    JEL classification:

    • A22 - General Economics and Teaching - - Economic Education and Teaching of Economics - - - Undergraduate
    • I20 - Health, Education, and Welfare - - Education - - - General

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