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Estimating Coherency between Survey Data and Incentivized Experimental Data

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  • Christian Belzil
  • Julie Pernaudet
  • François Poinas

Abstract

Imagine the situation in which an econometrician can infer the distribution of welfare gains induced by the provision of higher education financial aid using survey data obtained from a set of individuals, and can estimate the same distribution using a highly incentivized field experiment in which the same set of individuals participated. In the experimental setting relying on incentivized choices, making the wrong decision can be costly. In the survey, the stakes are null and reporting false intentions and expectations is costless. In this paper, we evaluate the extent to which the decomposition of the two welfare gain distributions into latent factors are coherent. We find that individuals often put a much different weight to a specific set of determinants in the experiment and in the survey and that the valuations of financial aid are rank incoherent. About 66% of Biased Incoherency (defined as the tendency to have a higher valuation rank in the experiment than in the survey) is explained by individual heterogeneity in subjective benefits, costs and other factors and about half of these factors affect the welfare gains of financial aid in the survey and in the experiment in opposite directions. Ex-ante policy evaluation of a potential expansion of the higher education financial aid system may therefore depend heavily on whether or not the data have been obtained in an incentivized context. Imaginez la situation dans laquelle un économètre peut déduire la distribution des gains de bien-être induits par l'octroi d'une aide financière à l'enseignement supérieur à l'aide de données d'enquête obtenues auprès d'un ensemble d'individus, et peut estimer la même distribution à l'aide d'une expérience de terrain fortement incitative à laquelle le même ensemble d'individus a participé. Dans le cadre expérimental reposant sur des choix incitatifs, prendre une mauvaise décision peut être coûteux. Dans l'enquête, l'enjeu est nul et la déclaration de fausses intentions et attentes est sans coût. Dans cet article, nous évaluons dans quelle mesure la décomposition des deux distributions de gains de bien-être en facteurs latents est cohérente. Nous constatons que les individus accordent souvent un poids très différent à un ensemble spécifique de déterminants dans l'expérience et dans l'enquête et que les évaluations de l'aide financière sont incohérentes. Environ 66% de l'incohérence biaisée (définie comme la tendance à avoir un rang d'évaluation plus élevé dans l'expérience que dans l'enquête) s'explique par l'hétérogénéité individuelle des avantages subjectifs, des coûts et d'autres facteurs et environ la moitié de ces facteurs affectent les gains de bien-être de l'aide financière dans l'enquête et dans l'expérience dans des directions opposées. L'évaluation politique ex ante d'une expansion potentielle du système d'aide financière à l'enseignement supérieur peut donc dépendre fortement du fait que les données ont été obtenues ou non dans un contexte incitatif.

Suggested Citation

  • Christian Belzil & Julie Pernaudet & François Poinas, 2021. "Estimating Coherency between Survey Data and Incentivized Experimental Data," CIRANO Working Papers 2021s-30, CIRANO.
  • Handle: RePEc:cir:cirwor:2021s-30
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    References listed on IDEAS

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    1. Benjamin Williams, 2018. "Identification of the Linear Factor Model," Working Papers 2018-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    2. Frenette, Marc, 2014. "An Investment of a Lifetime? The Long-term Labour Market Premiums Associated with a Postsecondary Education," Analytical Studies Branch Research Paper Series 2014359e, Statistics Canada, Analytical Studies Branch.
    3. Christian Belzil & Arnaud Maurel & Modibo Sidibé, 2021. "Estimating the Value of Higher Education Financial Aid: Evidence from a Field Experiment," Journal of Labor Economics, University of Chicago Press, vol. 39(2), pages 361-395.
    4. Levon Barseghyan & Francesca Molinari & Matthew Thirkettle, 2021. "Discrete Choice under Risk with Limited Consideration," American Economic Review, American Economic Association, vol. 111(6), pages 1972-2006, June.
    5. Belzil, Christian, 2007. "The return to schooling in structural dynamic models: a survey," European Economic Review, Elsevier, vol. 51(5), pages 1059-1105, July.
    6. Arcidiacono, Peter & Hotz, V. Joseph & Kang, Songman, 2012. "Modeling college major choices using elicited measures of expectations and counterfactuals," Journal of Econometrics, Elsevier, vol. 166(1), pages 3-16.
    7. Ralph Stinebrickner & Todd R. Stinebrickner, 2014. "A Major in Science? Initial Beliefs and Final Outcomes for College Major and Dropout," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(1), pages 426-472.
    8. Jared Ashworth & V. Joseph Hotz & Arnaud Maurel & Tyler Ransom, 2021. "Changes across Cohorts in Wage Returns to Schooling and Early Work Experiences," Journal of Labor Economics, University of Chicago Press, vol. 39(4), pages 931-964.
    9. Christian Belzil & Jörgen Hansen, 2002. "Unobserved Ability and the Return to Schooling," Econometrica, Econometric Society, vol. 70(5), pages 2075-2091, September.
    10. Filip Matêjka & Alisdair McKay, 2015. "Rational Inattention to Discrete Choices: A New Foundation for the Multinomial Logit Model," American Economic Review, American Economic Association, vol. 105(1), pages 272-298, January.
    11. James J. Heckman & John Eric Humphries & Gregory Veramendi, 2018. "Returns to Education: The Causal Effects of Education on Earnings, Health, and Smoking," Journal of Political Economy, University of Chicago Press, vol. 126(S1), pages 197-246.
    12. Lochner, L. & Monge-Naranjo, A., 2016. "Student Loans and Repayment," Handbook of the Economics of Education,, Elsevier.
    13. Patrick Bajari & Jeremy T. Fox & Stephen P. Ryan, 2007. "Linear Regression Estimation of Discrete Choice Models with Nonparametric Distributions of Random Coefficients," American Economic Review, American Economic Association, vol. 97(2), pages 459-463, May.
    14. Benjamin Williams, 2020. "Identification of the linear factor model," Econometric Reviews, Taylor & Francis Journals, vol. 39(1), pages 92-109, January.
    15. Uri Gneezy & John A. List & Jeffrey A. Livingston & Xiangdong Qin & Sally Sadoff & Yang Xu, 2019. "Measuring Success in Education: The Role of Effort on the Test Itself," American Economic Review: Insights, American Economic Association, vol. 1(3), pages 291-308, December.
    16. Matthew Wiswall & Basit Zafar, 2015. "Determinants of College Major Choice: Identification using an Information Experiment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(2), pages 791-824.
    17. Peter Arcidiacono, 2005. "Affirmative Action in Higher Education: How Do Admission and Financial Aid Rules Affect Future Earnings?," Econometrica, Econometric Society, vol. 73(5), pages 1477-1524, September.
    18. Adeline Delavande & Basit Zafar, 2019. "University Choice: The Role of Expected Earnings, Nonpecuniary Outcomes, and Financial Constraints," Journal of Political Economy, University of Chicago Press, vol. 127(5), pages 2343-2393.
    19. Tomáš Jagelka, 2020. "Are Economists’ Preferences Psychologists’ Personality Traits? A Structural Approach," ECONtribute Discussion Papers Series 014, University of Bonn and University of Cologne, Germany.
    20. Yifan Gong & Lance Lochner & Ralph Stinebrickner & Todd R. Stinebrickner, 2019. "The Consumption Value of College," NBER Working Papers 26335, National Bureau of Economic Research, Inc.
    21. Ola Andersson & Håkan J. Holm & Jean-Robert Tyran & Erik Wengström, 2020. "Robust inference in risk elicitation tasks," Journal of Risk and Uncertainty, Springer, vol. 61(3), pages 195-209, December.
    22. Manski, Charles F. & Molinari, Francesca, 2010. "Rounding Probabilistic Expectations in Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 219-231.
    23. Zvi Eckstein & Kenneth I. Wolpin, 1999. "Why Youths Drop Out of High School: The Impact of Preferences, Opportunities, and Abilities," Econometrica, Econometric Society, vol. 67(6), pages 1295-1340, November.
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    More about this item

    Keywords

    Field experiment; survey data; coherency; incentives; Expérience sur le terrain; données d'enquête; cohérence; incitations;
    All these keywords.

    JEL classification:

    • I2 - Health, Education, and Welfare - - Education
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D9 - Microeconomics - - Micro-Based Behavioral Economics
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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