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(No) Trade-off between numeracy and verbal reasoning development: PISA evidence from Italy's academic tracking

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  • Zullo, Matteo

Abstract

The study uses PISA data to evaluate cognitive development trade-offs between numeracy and literacy skills. The compendious literature validating the educational and financial gains from technical education fails to address the potential underdevelopment of verbal skills. Exploiting academic tracking in Italy’s high school education with distinctive Liberal Arts (n = 841) and STEM (n = 1968) pathways, the study rules out any cognitive trade-off and estimates the STEM premium on the reading section at about one-fifth of an international standard deviation (20 PISA points). Decomposition of the education production function reveals that the technical track outperforms Liberal Arts due to greater educational production efficiency overcompensating for worse educational production inputs. Further regression analysis links the STEM advantage to the four additional instructional units in math and physics. Robustness checks conducted using TIMSS and PIRLS test scores exclude that effects are secondary to differences in preexisting levels of student skills.11I acknowledge that this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No conflicts of interests are recognized.

Suggested Citation

  • Zullo, Matteo, 2022. "(No) Trade-off between numeracy and verbal reasoning development: PISA evidence from Italy's academic tracking," Intelligence, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:intell:v:95:y:2022:i:c:s0160289622000848
    DOI: 10.1016/j.intell.2022.101703
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    1. Daniele Checchi & Luca Flabbi, 2013. "Intergenerational Mobility and Schooling Decisions in Germany and Italy: The Impact of Secondary School Tracks," Rivista di Politica Economica, SIPI Spa, issue 3, pages 7-57, July-Sept.
    2. Levine, Phillip B & Zimmerman, David J, 1995. "The Benefit of Additional High-School Math and Science Classes for Young Men and Women," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 137-149, April.
    3. Piopiunik, Marc, 2014. "The effects of early tracking on student performance: Evidence from a school reform in Bavaria," Economics of Education Review, Elsevier, vol. 42(C), pages 12-33.
    4. James Heckman & Flavio Cunha, 2007. "The Technology of Skill Formation," American Economic Review, American Economic Association, vol. 97(2), pages 31-47, May.
    5. Cortes, Kalena E. & Goodman, Joshua Samuel & Nomi, Takako, 2015. "Intensive Math Instruction and Educational Attainment," Scholarly Articles 34298862, Harvard Kennedy School of Government.
    6. Hanushek, Eric A. & Woessmann, Ludger, 2011. "The Economics of International Differences in Educational Achievement," Handbook of the Economics of Education, in: Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), Handbook of the Economics of Education, edition 1, volume 3, chapter 2, pages 89-200, Elsevier.
    7. repec:fth:prinin:366 is not listed on IDEAS
    8. 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.
    9. Coyle, Thomas R., 2018. "Non-g residuals of group factors predict ability tilt, college majors, and jobs: A non-g nexus," Intelligence, Elsevier, vol. 67(C), pages 19-25.
    10. Joshua Goodman, 2019. "The Labor of Division: Returns to Compulsory High School Math Coursework," Journal of Labor Economics, University of Chicago Press, vol. 37(4), pages 1141-1182.
    11. Sass, Tim R. & Semykina, Anastasia & Harris, Douglas N., 2014. "Value-added models and the measurement of teacher productivity," Economics of Education Review, Elsevier, vol. 38(C), pages 9-23.
    12. Eric A. Hanushek, 1979. "Conceptual and Empirical Issues in the Estimation of Educational Production Functions," Journal of Human Resources, University of Wisconsin Press, vol. 14(3), pages 351-388.
    13. Oaxaca, Ronald, 1973. "Male-Female Wage Differentials in Urban Labor Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(3), pages 693-709, October.
    14. Joseph G. Altonji, 1995. "The Effects of High School Curriculum on Education and Labor Market Outcomes," Journal of Human Resources, University of Wisconsin Press, vol. 30(3), pages 409-438.
    15. Flavio Cunha & James J. Heckman & Susanne M. Schennach, 2010. "Estimating the Technology of Cognitive and Noncognitive Skill Formation," Econometrica, Econometric Society, vol. 78(3), pages 883-931, May.
    16. David Card & Alan B. Krueger, 1996. "School Resources and Student Outcomes: An Overview of the Literature and New Evidence from North and South Carolina," Journal of Economic Perspectives, American Economic Association, vol. 10(4), pages 31-50, Fall.
    17. Petra E. Todd & Kenneth I. Wolpin, 2003. "On The Specification and Estimation of The Production Function for Cognitive Achievement," Economic Journal, Royal Economic Society, vol. 113(485), pages 3-33, February.
    18. Doyle, Orla & Harmon, Colm & Heckman, James J. & Logue, Caitriona & Moon, Seong Hyeok, 2017. "Early skill formation and the efficiency of parental investment: A randomized controlled trial of home visiting," Labour Economics, Elsevier, vol. 45(C), pages 40-58.
    19. Alan S. Blinder, 1973. "Wage Discrimination: Reduced Form and Structural Estimates," Journal of Human Resources, University of Wisconsin Press, vol. 8(4), pages 436-455.
    20. Pekkarinen, Tuomas & Uusitalo, Roope & Kerr, Sari, 2009. "School tracking and intergenerational income mobility: Evidence from the Finnish comprehensive school reform," Journal of Public Economics, Elsevier, vol. 93(7-8), pages 965-973, August.
    21. Juanna Schrøter Joensen & Helena Skyt Nielsen, 2009. "Is there a Causal Effect of High School Math on Labor Market Outcomes?," Journal of Human Resources, University of Wisconsin Press, vol. 44(1).
    22. David Card & Alan Krueger, 1996. "School Resources and Student Outcomes: An Overview of the Literature and New Evidence from North and South Carolina," Working Papers 745, Princeton University, Department of Economics, Industrial Relations Section..
    23. Heather Rose & Julian R. Betts, 2004. "The Effect of High School Courses on Earnings," The Review of Economics and Statistics, MIT Press, vol. 86(2), pages 497-513, May.
    24. Wai, Jonathan & Hodges, Jaret & Makel, Matthew C., 2018. "Sex differences in ability tilt in the right tail of cognitive abilities: A 35-year examination," Intelligence, Elsevier, vol. 67(C), pages 76-83.
    25. Eric A. Hanushek, 2003. "The Failure of Input-Based Schooling Policies," Economic Journal, Royal Economic Society, vol. 113(485), pages 64-98, February.
    26. Heckman, James J, 1976. "A Life-Cycle Model of Earnings, Learning, and Consumption," Journal of Political Economy, University of Chicago Press, vol. 84(4), pages 11-44, August.
    27. C. Kirabo Jackson & Rucker C. Johnson & Claudia Persico, 2016. "The Effects of School Spending on Educational and Economic Outcomes: Evidence from School Finance Reforms," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(1), pages 157-218.
    28. Tamm, Marcus, 2008. "Does money buy higher schooling?: Evidence from secondary school track choice in Germany," Economics of Education Review, Elsevier, vol. 27(5), pages 536-545, October.
    29. Eric A Hanushek & Lavinia Kinneifo & Philipp Lergetporer & Ludger Woessmann, 2022. "Patience, Risk-Taking, and Human Capital Investment Across Countries," The Economic Journal, Royal Economic Society, vol. 132(646), pages 2290-2307.
    30. Becker, David & Coyle, Thomas R. & Minnigh, Tyler L. & Rindermann, Heiner, 2022. "International differences in math and science tilts: The stability, geography, and predictive power of tilt for economic criteria," Intelligence, Elsevier, vol. 92(C).
    31. Burhan, Nik Ahmad Sufian & Mohamad, Mohd Rosli & Kurniawan, Yohan & Sidek, Abdul Halim, 2014. "The Impact of Low, Average, and High IQ on Economic Growth and Technological Progress: Do All Individuals Contribute Equally?," MPRA Paper 77321, University Library of Munich, Germany.
    32. Mark C. Long & Patrice Iatarola & Dylan Conger, 2009. "Explaining Gaps in Readiness for College-Level Math: The Role of High School Courses," Education Finance and Policy, MIT Press, vol. 4(1), pages 1-33, January.
    33. Pokropek, Artur & Marks, Gary N. & Borgonovi, Francesca & Koc, Piotr & Greiff, Samuel, 2022. "General or specific abilities? Evidence from 33 countries participating in the PISA assessments," Intelligence, Elsevier, vol. 92(C).
    34. Jihyun Lee, 2020. "Non-cognitive characteristics and academic achievement in Southeast Asian countries based on PISA 2009, 2012, and 2015," OECD Education Working Papers 233, OECD Publishing.
    35. Garett Jones & Niklas Potrafke, 2014. "Human Capital and National Institutional Quality: Are TIMSS, PISA, and National Average IQ Robust Predictors?," CESifo Working Paper Series 4790, CESifo.
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