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A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills

Author

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  • Knaus, Michael C.

    () (University of St. Gallen)

Abstract

This study investigates the dose-response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study proposes solutions to two highly practically relevant questions that arise for these new methods: (i) How to investigate sensitivity of estimates to tuning parameter choices in the machine learning part? (ii) How to assess covariate balancing in high-dimensional settings? The results show that improvements in objectively measured cognitive skills require at least medium intensity, while improvements in school grades are already observed for low intensity of practice.

Suggested Citation

  • Knaus, Michael C., 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," IZA Discussion Papers 11547, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp11547
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    References listed on IDEAS

    as
    1. Felfe, Christina & Lechner, Michael & Steinmayr, Andreas, 2011. "Sport and Child Development," Economics Working Paper Series 1135, University of St. Gallen, School of Economics and Political Science.
    2. Charlotte Cabane & Adrian Hille & Michael Lechner, 2015. "Mozart or Pelé? The Effects of Teenagers' Participation in Music and Sports," SOEPpapers on Multidisciplinary Panel Data Research 749, DIW Berlin, The German Socio-Economic Panel (SOEP).
    3. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    4. Hainmueller, Jens, 2012. "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies," Political Analysis, Cambridge University Press, vol. 20(01), pages 25-46, December.
    5. Hille, Adrian & Schupp, Jürgen, 2015. "How learning a musical instrument affects the development of skills," Economics of Education Review, Elsevier, vol. 44(C), pages 56-82.
    6. Cattaneo, Matias D., 2010. "Efficient semiparametric estimation of multi-valued treatment effects under ignorability," Journal of Econometrics, Elsevier, vol. 155(2), pages 138-154, April.
    7. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    8. Leeb, Hannes & P tscher, Benedikt M., 2005. "Model Selection And Inference: Facts And Fiction," Econometric Theory, Cambridge University Press, vol. 21(01), pages 21-59, February.
    9. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    10. Bryan S. Graham & Cristine Campos de Xavier Pinto & Daniel Egel, 2016. "Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 288-301, April.
    11. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 1053-1079.
    12. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    13. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," Review of Economic Studies, Oxford University Press, vol. 81(2), pages 608-650.
    14. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
    15. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    16. repec:nbr:nberch:14009 is not listed on IDEAS
    17. Lechner, Michael & Strittmatter, Anthony, 2014. "Practical Procedures to Deal with Common Support Problems in Matching Estimation," Economics Working Paper Series 1410, University of St. Gallen, School of Economics and Political Science.
    18. repec:wly:emetrp:v:85:y:2017:i::p:233-298 is not listed on IDEAS
    19. repec:bla:biomet:v:72:y:2016:i:4:p:1055-1065 is not listed on IDEAS
    20. Lechner, Michael, 2008. "A note on endogenous control variables in causal studies," Statistics & Probability Letters, Elsevier, vol. 78(2), pages 190-195, February.
    21. A. Belloni & V. Chernozhukov & I. Fernández‐Val & C. Hansen, 2017. "Program Evaluation and Causal Inference With High‐Dimensional Data," Econometrica, Econometric Society, vol. 85, pages 233-298, January.
    22. Cabane, Charlotte & Hille, Adrian & Lechner, Michael, 2016. "Mozart or Pelé? The effects of adolescents' participation in music and sports," Labour Economics, Elsevier, vol. 41(C), pages 90-103.
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    More about this item

    Keywords

    double machine learning; extracurricular activities; music; cognitive and non-cognitive skills; youth development;

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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