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Nonparametric Bounds on the Causal Effect of University Studies on Job Opportunities Using Principal Stratification

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  • Leonardo Grilli
  • Fabrizia Mealli

Abstract

The authors propose a methodology based on the principal strata approach to causal inference for assessing the relative effectiveness of two degree programs with respect to the employment status of their graduates. An innovative use of nonparametric bounds in the principal strata framework is shown, examining the role of some assumptions in reducing uncertainty about the causal effects and proposing a strategy to use the covariates in the construction of the bounds. In the application, the nonparametric bounds turn out to be quite informative on the average causal effect for the latent group of students who are potentially able to graduate from both degree programs. There is some evidence that the effect is positive for economics with respect to political science, at least for some values of the covariates.

Suggested Citation

  • Leonardo Grilli & Fabrizia Mealli, 2008. "Nonparametric Bounds on the Causal Effect of University Studies on Job Opportunities Using Principal Stratification," Journal of Educational and Behavioral Statistics, , vol. 33(1), pages 111-130, March.
    • Handle: RePEc:sae:jedbes:v:33:y:2008:i:1:p:111-130
    DOI: 10.3102/1076998607302627
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    Citations

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    Cited by:

    1. Cavalletti, Barbara & Corsi, Matteo & Persico, Luca & di Bella, Enrico, 2021. "Public university orientation for high-school students. A quasi-experimental assessment of the efficiency gains from nudging better career choices," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    2. Martin Huber & Giovanni Mellace, 2015. "Sharp Bounds on Causal Effects under Sample Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 129-151, February.
    3. Kaitlin Anderson & Gema Zamarro & Jennifer Steele & Trey Miller, 2021. "Comparing Performance of Methods to Deal With Differential Attrition in Randomized Experimental Evaluations," Evaluation Review, , vol. 45(1-2), pages 70-104, February.
    4. Fabrizia Mealli & Barbara Pacini & Elena Stanghellini, 2016. "Identification of Principal Causal Effects Using Additional Outcomes in Concentration Graphs," Journal of Educational and Behavioral Statistics, , vol. 41(5), pages 463-480, October.
    5. Shanshan Luo & Wei Li & Yangbo He, 2023. "Causal inference with outcomes truncated by death in multiarm studies," Biometrics, The International Biometric Society, vol. 79(1), pages 502-513, March.
    6. Jiannan Lu & Peng Ding & Tirthankar Dasgupta, 2018. "Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 540-567, October.
    7. Dustin M. Long & Michael G. Hudgens, 2013. "Sharpening Bounds on Principal Effects with Covariates," Biometrics, The International Biometric Society, vol. 69(4), pages 812-819, December.
    8. Sandra De Iaco & Sabrina Maggio & Donato Posa, 2019. "A Multilevel Multinomial Model for the Dynamics of Graduates Employment in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 149-168, November.
    9. Avi Feller & Fabrizia Mealli & Luke Miratrix, 2017. "Principal Score Methods: Assumptions, Extensions, and Practical Considerations," Journal of Educational and Behavioral Statistics, , vol. 42(6), pages 726-758, December.
    10. Jiannan Lu & Yunshu Zhang & Peng Ding, 2020. "Sharp bounds on the relative treatment effect for ordinal outcomes," Biometrics, The International Biometric Society, vol. 76(2), pages 664-669, June.
    11. Kédagni, Désiré, 2023. "Identifying treatment effects in the presence of confounded types," Journal of Econometrics, Elsevier, vol. 234(2), pages 479-511.
    12. Montserrat Hernández-LÓPEZ & José Juan Cáceres-HERNÁNDEZ, 2016. "Forecasting The Composition Of Demand For Higher Education Degrees By Genetic Algorithms," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(3), pages 153-172.
    13. Andrea Mercatanti & Fan Li, 2017. "Do debit cards decrease cash demand?: causal inference and sensitivity analysis using principal stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 759-776, August.

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