IDEAS home Printed from https://ideas.repec.org/p/zbw/glodps/511.html
   My bibliography  Save this paper

Teaching ‘out of field’ in STEM subjects in Australia: Evidence from PISA 2015

Author

Listed:
  • Shah, Chandra
  • Richardson, Paul
  • Watt, Helen

Abstract

Science, technology, engineering and mathematics (STEM) education is a critical part of a modern education system. Motivating students to learn STEM subjects is however a challenge. Teachers have a critical role in motivating students but to do this effectively they need to have appropriate subject matter knowledge. Data from PISA 2015 show a substantial proportion of teachers in Australian schools are teaching STEM subjects ‘out-of-field’, which is that they do not have the qualifications to teach these subjects. This paper examines the effects of individual teacher characteristics and school context on of out-of-field teaching in STEM subjects. In particular, it examines the role of school autonomy and staff shortage in this. The results show these two variables have a strong association with out-of-field teaching, however, other factors either mediate or confound their effects. A full understanding of the results requires knowing the role of school funding and school budgets in out-of-field teaching. While we do not have direct measures of these in the data, we can infer their likely roles through the effects of other factors, such as school sector and education level of parents of students in the school, in the model.

Suggested Citation

  • Shah, Chandra & Richardson, Paul & Watt, Helen, 2020. "Teaching ‘out of field’ in STEM subjects in Australia: Evidence from PISA 2015," GLO Discussion Paper Series 511, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:511
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/215639/1/GLO-DP-0511.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Charles T. Clotfelter & Helen F. Ladd & Jacob L. Vigdor, 2010. "Teacher Credentials and Student Achievement in High School: A Cross-Subject Analysis with Student Fixed Effects," Journal of Human Resources, University of Wisconsin Press, vol. 45(3).
    2. Clotfelter, Charles T. & Ladd, Helen F. & Vigdor, Jacob L., 2007. "Teacher credentials and student achievement: Longitudinal analysis with student fixed effects," Economics of Education Review, Elsevier, vol. 26(6), pages 673-682, December.
    3. Richard Williams, 2012. "Using the margins command to estimate and interpret adjusted predictions and marginal effects," Stata Journal, StataCorp LLC, vol. 12(2), pages 308-331, June.
    4. Greene, William, 2010. "Testing hypotheses about interaction terms in nonlinear models," Economics Letters, Elsevier, vol. 107(2), pages 291-296, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dan Goldhaber & Roddy Theobald, 2013. "Managing the Teacher Workforce in Austere Times: The Determinants and Implications of Teacher Layoffs," Education Finance and Policy, MIT Press, vol. 8(4), pages 494-527, October.
    2. Papay, John P. & Kraft, Matthew A., 2015. "Productivity returns to experience in the teacher labor market: Methodological challenges and new evidence on long-term career improvement," Journal of Public Economics, Elsevier, vol. 130(C), pages 105-119.
    3. Seth Gershenson, 2016. "Performance Standards and Employee Effort: Evidence From Teacher Absences," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(3), pages 615-638, June.
    4. Brache, Jose & Felzensztein, Christian, 2019. "Exporting firm’s engagement with trade associations: Insights from Chile," International Business Review, Elsevier, vol. 28(1), pages 25-35.
    5. Rebecca Allen & Jay Allnutt, 2013. "Matched panel data estimates of the impact of Teach First on school and departmental performance," DoQSS Working Papers 13-11, Quantitative Social Science - UCL Social Research Institute, University College London.
    6. Buddin, Richard, 2010. "How effective are Los Angeles elementary teachers and schools?," MPRA Paper 27366, University Library of Munich, Germany.
    7. Canales, Andrea & Maldonado, Luis, 2018. "Teacher quality and student achievement in Chile: Linking teachers' contribution and observable characteristics," International Journal of Educational Development, Elsevier, vol. 60(C), pages 33-50.
    8. Helen F. Ladd & Lucy C. Sorensen, 2017. "Returns to Teacher Experience: Student Achievement and Motivation in Middle School," Education Finance and Policy, MIT Press, vol. 12(2), pages 241-279, Spring.
    9. Timothy M. Diette & Ruth Uwaifo Oyelere, 2017. "Do limited English students jeopardize the education of other students? Lessons from the North Carolina public school system," Education Economics, Taylor & Francis Journals, vol. 25(5), pages 446-461, September.
    10. Cook, Jason B. & Mansfield, Richard K., 2016. "Task-specific experience and task-specific talent: Decomposing the productivity of high school teachers," Journal of Public Economics, Elsevier, vol. 140(C), pages 51-72.
    11. Seah, Kelvin KC, 2018. "Immigrant educators and students’ academic achievement," Labour Economics, Elsevier, vol. 51(C), pages 152-169.
    12. Lee, Se Woong & Lee, Eunjung Alice, 2020. "Teacher qualification matters: The association between cumulative teacher qualification and students’ educational attainment," International Journal of Educational Development, Elsevier, vol. 77(C).
    13. Gary Henry & Roderick Rose & Doug Lauen, 2014. "Are value-added models good enough for teacher evaluations? Assessing commonly used models with simulated and actual data," Investigaciones de Economía de la Educación volume 9, in: Adela García Aracil & Isabel Neira Gómez (ed.), Investigaciones de Economía de la Educación 9, edition 1, volume 9, chapter 20, pages 383-405, Asociación de Economía de la Educación.
    14. Lahoti, Rahul & Sahoo, Soham, 2020. "Are educated leaders good for education? Evidence from India," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 42-62.
    15. Diette, Timothy M. & Uwaifo Oyelere, Ruth, 2012. "Do Significant Immigrant Inflows Create Negative Education Impacts? Lessons from the North Carolina Public School System," IZA Discussion Papers 6561, Institute of Labor Economics (IZA).
    16. Vera Freundl & Pietro Sancassani, 2023. "The Effect of Subject-Specific Teacher Qualifications on Student Science Achievement," EconPol Policy Brief 51, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    17. Rodrigues, Clarissa Guimarães & Rios-Neto, Eduardo Luiz Gonçalves & de Xavier Pinto, Cristine Campos, 2013. "Changes in test scores distribution for students of the fourth grade in Brazil: A relative distribution analysis for the years 1997–2005," Economics of Education Review, Elsevier, vol. 34(C), pages 227-242.
    18. Hill, Andrew J. & Jones, Daniel B., 2018. "A teacher who knows me: The academic benefits of repeat student-teacher matches," Economics of Education Review, Elsevier, vol. 64(C), pages 1-12.
    19. Figlio, D. & Karbownik, K. & Salvanes, K.G., 2016. "Education Research and Administrative Data," Handbook of the Economics of Education,, Elsevier.
    20. Berlinski, Samuel & Ramos, Alejandra, 2020. "Teacher mobility and merit pay: Evidence from a voluntary public award program," Journal of Public Economics, Elsevier, vol. 186(C).

    More about this item

    Keywords

    out-of-field teaching; teacher supply and demand; multi-level logit model;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:glodps:511. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/glabode.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.