IDEAS home Printed from https://ideas.repec.org/p/ake/iiepdt/202165.html
   My bibliography  Save this paper

MENTORING AS A DOSE TREATMENT: FREQUENCY MATTERS: Evidence from a French mentoring program

Author

Listed:
  • Gabriel Montes-Rojas

    (Instituto Interdisciplinario de Economía Política de Buenos Aires - UBA - CONICET)

  • Vera Chiodi

    (Universit e de la Sorbonne)

Abstract

We evaluate how the impact of a mentoring program in French disadvantaged high schools varies with the intensity of the program. Given that, in general, the only significant effect was observed by full attendance to all meetings, we argue that the treatment dose matters. Thus, while the original evaluation program was designed as a randomized experiment to balance control and treated individuals (those who were offered the mentoring scheme, with diferent degree of program participation), we motivate the use of continuous and multi-valued treatment effects models to estimate the dose response function. The program shows that information about prospective labor market opportunities feeds back positively into academic performance. However, it has a negative effect on job self-esteem, suggesting that acquiring information on job market prospects updates students’ priors on their skills and possibilities and that the students might be updating rationally.

Suggested Citation

  • Gabriel Montes-Rojas & Vera Chiodi, 2021. "MENTORING AS A DOSE TREATMENT: FREQUENCY MATTERS: Evidence from a French mentoring program," Documentos de trabajo del Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET) 2021-65, Universidad de Buenos Aires, Facultad de Ciencias Económicas, Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET).
    • Handle: RePEc:ake:iiepdt:202165
    as

    Download full text from publisher

    File URL: https://iiep-baires.econ.uba.ar/uploads/publicaciones/537/archivos/1.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. J. P. Florens & J. J. Heckman & C. Meghir & E. Vytlacil, 2008. "Identification of Treatment Effects Using Control Functions in Models With Continuous, Endogenous Treatment and Heterogeneous Effects," Econometrica, Econometric Society, vol. 76(5), pages 1191-1206, September.
    2. Tacsir, Ezequiel, 2010. "Occupation Choice: Family, Social and Market Influences," MERIT Working Papers 2010-013, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    3. Matias D. Cattaneo & David M. Drukker & Ashley D. Holland, 2013. "Estimation of multivalued treatment effects under conditional independence," Stata Journal, StataCorp LP, vol. 13(3), pages 407-450, September.
    4. 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.
    5. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    6. Roland Bénabou & Jean Tirole, 2005. "Self-Confidence and Personal Motivation," International Economic Association Series, in: Bina Agarwal & Alessandro Vercelli (ed.), Psychology, Rationality and Economic Behaviour, chapter 2, pages 19-57, Palgrave Macmillan.
    7. Eric Bettinger, 2004. "How Financial Aid Affects Persistence," NBER Chapters, in: College Choices: The Economics of Where to Go, When to Go, and How to Pay For It, pages 207-238, National Bureau of Economic Research, Inc.
    8. Michela Bia & Philippe Van Kerm, 2014. "Space-filling location selection," Stata Journal, StataCorp LP, vol. 14(3), pages 605-622, September.
    9. Philip Oreopoulos & Daniel Lang & Joshua Angrist, 2009. "Incentives and Services for College Achievement: Evidence from a Randomized Trial," American Economic Journal: Applied Economics, American Economic Association, vol. 1(1), pages 136-163, January.
    10. Orazio Attanasio & Katja Kaufmann, 2009. "Educational Choices, Subjective Expectations, and Credit Constraints," NBER Working Papers 15087, National Bureau of Economic Research, Inc.
    11. Lorraine Dearden & Carl Emmerson & Christine Frayne & Costas Meghir, 2005. "Education subsidies and school drop-out rates," IFS Working Papers W05/11, Institute for Fiscal Studies.
    12. Susan M. Dynarski, 2003. "Does Aid Matter? Measuring the Effect of Student Aid on College Attendance and Completion," American Economic Review, American Economic Association, vol. 93(1), pages 279-288, March.
    13. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    14. Guido W. Imbens & Whitney Newey & Geert Ridder, 2005. "Mean-square-error Calculations for Average Treatment Effects," IEPR Working Papers 05.34, Institute of Economic Policy Research (IEPR).
    15. Carlos A. Flores, 2007. "Estimation of Dose-Response Functions and Optimal Doses with a Continuous Treatment," Working Papers 0707, University of Miami, Department of Economics.
    16. Caroline Hoxby & Christopher Avery, 2013. "The Missing "One-Offs": The Hidden Supply of High-Achieving, Low-Income Students," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 44(1 (Spring), pages 1-65.
    17. Antonio F. Galvao & Liang Wang, 2015. "Uniformly Semiparametric Efficient Estimation of Treatment Effects With a Continuous Treatment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1528-1542, December.
    18. Michela Bia & Carlos A. Flores & Alfonso Flores-Lagunes & Alessandra Mattei, 2014. "A Stata package for the application of semiparametric estimators of dose–response functions," Stata Journal, StataCorp LP, vol. 14(3), pages 580-604, September.
    19. Carlos A. Flores & Alfonso Flores-Lagunes & Arturo Gonzalez & Todd C. Neumann, 2012. "Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 153-171, February.
    20. Chung Choe & Alfonso Flores-Lagunes & Sang-Jun Lee, 2015. "Do dropouts with longer training exposure benefit from training programs? Korean evidence employing methods for continuous treatments," Empirical Economics, Springer, vol. 48(2), pages 849-881, March.
    21. 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.
    22. Alejo, Javier & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2018. "Quantile continuous treatment effects," Econometrics and Statistics, Elsevier, vol. 8(C), pages 13-36.
    23. Li, Qi & Racine, Jeffrey S. & Wooldridge, Jeffrey M., 2009. "Efficient Estimation of Average Treatment Effects with Mixed Categorical and Continuous Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 206-223.
    24. Robert Jensen, 2010. "The (Perceived) Returns to Education and the Demand for Schooling," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(2), pages 515-548.
    25. Michela Bia & Alessandra Mattei, 2008. "A Stata package for the estimation of the dose–response function through adjustment for the generalized propensity score," Stata Journal, StataCorp LP, vol. 8(3), pages 354-373, September.
    26. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    27. Eric P. Bettinger & Bridget Terry Long & Philip Oreopoulos & Lisa Sanbonmatsu, 2012. "The Role of Application Assistance and Information in College Decisions: Results from the H&R Block Fafsa Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1205-1242.
    28. Yona Rubinstein & James J. Heckman, 2001. "The Importance of Noncognitive Skills: Lessons from the GED Testing Program," American Economic Review, American Economic Association, vol. 91(2), pages 145-149, 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. Alejo, Javier & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2018. "Quantile continuous treatment effects," Econometrics and Statistics, Elsevier, vol. 8(C), pages 13-36.
    2. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    3. Flores, Carlos A. & Mitnik, Oscar A., 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," IZA Discussion Papers 4451, Institute of Labor Economics (IZA).
    4. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.
    5. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    6. Ai, Chunrong & Linton, Oliver & Zhang, Zheng, 2022. "Estimation and inference for the counterfactual distribution and quantile functions in continuous treatment models," Journal of Econometrics, Elsevier, vol. 228(1), pages 39-61.
    7. Roberto ESPOSTI, 2014. "To match, not to match, how to match: Estimating the farm-level impact of the CAP-first pillar reform (or: How to Apply Treatment-Effect Econometrics when the Real World is;a Mess)," Working Papers 403, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    8. Aguirre, Josefa & Matta, Juan, 2021. "Walking in your footsteps: Sibling spillovers in higher education choices," Economics of Education Review, Elsevier, vol. 80(C).
    9. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2021. "A unified framework for efficient estimation of general treatment models," Quantitative Economics, Econometric Society, vol. 12(3), pages 779-816, July.
    10. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand," NBER Technical Working Papers 0330, National Bureau of Economic Research, Inc.
    11. Andrews, Rodney J. & Imberman, Scott A. & Lovenheim, Michael F., 2020. "Recruiting and supporting low-income, high-achieving students at flagship universities," Economics of Education Review, Elsevier, vol. 74(C).
    12. 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.
    13. Dehejia Rajeev, 2015. "Experimental and Non-Experimental Methods in Development Economics: A Porous Dialectic," Journal of Globalization and Development, De Gruyter, vol. 6(1), pages 47-69, June.
    14. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    15. Ferreira, Francisco H. G. & Firpo, Sergio & Galvao, Antonio F., 2017. "Estimation and Inference for Actual and Counterfactual Growth Incidence Curves," IZA Discussion Papers 10473, Institute of Labor Economics (IZA).
    16. Yukitoshi Matsushita & Taisuke Otsu & Keisuke Takahata, 2022. "Estimating density ratio of marginals to joint: Applications to causal inference," STICERD - Econometrics Paper Series 619, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    17. Max H. Farrell, 2013. "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations," Papers 1309.4686, arXiv.org, revised Feb 2018.
    18. Joo, Joonhwi & LaLonde, Robert J., 2014. "Testing for Selection Bias," IZA Discussion Papers 8455, Institute of Labor Economics (IZA).
    19. Kerr, Sari Pekkala & Pekkarinen, Tuomas & Sarvimäki, Matti & Uusitalo, Roope, 2020. "Post-secondary education and information on labor market prospects: A randomized field experiment," Labour Economics, Elsevier, vol. 66(C).
    20. Judith Scott-Clayton, 2012. "Information Constraints and Financial Aid Policy," NBER Working Papers 17811, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    Mentoring; Treatment effects; Dropout;
    All these keywords.

    JEL classification:

    • 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
    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I26 - Health, Education, and Welfare - - Education - - - Returns to Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

    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:ake:iiepdt:202165. 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: IIEP UBA-CONICET (email available below). General contact details of provider: https://edirc.repec.org/data/ieeubar.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.