IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v10y2020i1p2158244019898815.html
   My bibliography  Save this article

Self-Assessment Variables as a Source of Information in the Evaluation of Intervention Programs: A Theoretical and Methodological Framework

Author

Listed:
  • Yonatan Eyal

Abstract

The article discusses the incorporation of individuals’ assessments regarding the effect of intervention program on themselves as a source of information in commonly used quantitative program evaluation methods. The incorporation of Self-Assessment Variables ( SAV ) into the evaluation process enables the researcher to utilize the information contained in SAV while utilizing other available sources of information as well (such as administrative data). The analysis is based on the assumption that individuals possess valuable and unique information which they employ before self-selection into a program. The theory of planned behavior is used as a framework for examining different aspects of integrating SAV into program evaluation. The article elaborates on the integration of SAV into the matching method and on the possible advantages of that approach. In addition, the article discusses different aspects of the process of eliciting SAV from individuals. Finally, the article outlines possible directions for future research.

Suggested Citation

  • Yonatan Eyal, 2020. "Self-Assessment Variables as a Source of Information in the Evaluation of Intervention Programs: A Theoretical and Methodological Framework," SAGE Open, , vol. 10(1), pages 21582440198, January.
    • Handle: RePEc:sae:sagope:v:10:y:2020:i:1:p:2158244019898815
    DOI: 10.1177/2158244019898815
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2158244019898815
    Download Restriction: no
    File URL: https://libkey.io/10.1177/2158244019898815?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-455, June.
    2. James J. Heckman & Jeffrey A. Smith, 1995. "Assessing the Case for Social Experiments," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 85-110, Spring.
    3. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    4. Jeff Dominitz & Charles F. Manski & Jordan Heinz, 2003. ""Will Social Security Be There For You?": How Americans Perceive Their Benefits," NBER Working Papers 9798, National Bureau of Economic Research, Inc.
    5. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
    6. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
    7. James J. Heckman & Jeffrey A. Smith, 1998. "Evaluating the Welfare State," NBER Working Papers 6542, National Bureau of Economic Research, Inc.
    8. Caliendo, Marco & Mahlstedt, Robert & Mitnik, Oscar A., 2017. "Unobservable, but unimportant? The relevance of usually unobserved variables for the evaluation of labor market policies," Labour Economics, Elsevier, vol. 46(C), pages 14-25.
    9. 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.
    10. F. Thomas Juster, 1966. "Consumer Buying Intentions and Purchase Probability: An Experiment in Survey Design," NBER Books, National Bureau of Economic Research, Inc, number just66-2.
    11. 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.
    12. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
    13. 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.
    14. Thomas D. Cook & William R. Shadish & Vivian C. Wong, 2008. "Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 724-750.
    15. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    16. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
    17. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    18. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    19. Howard White, 2009. "Theory-based impact evaluation: principles and practice," Journal of Development Effectiveness, Taylor & Francis Journals, vol. 1(3), pages 271-284.
    20. Jeff Dominitz, 1998. "Earnings Expectations, Revisions, And Realizations," The Review of Economics and Statistics, MIT Press, vol. 80(3), pages 374-388, August.
    21. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    22. Naila Kabeer, 2019. "Randomized Control Trials and Qualitative Evaluations of a Multifaceted Programme for Women in Extreme Poverty: Empirical Findings and Methodological Reflections," Journal of Human Development and Capabilities, Taylor & Francis Journals, vol. 20(2), pages 197-217, April.
    23. Adeline Delavande, 2014. "Probabilistic Expectations in Developing Countries," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 1-20, August.
    24. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097, Elsevier.
    25. White, Howard, 2009. "Theory-Based Impact Evaluation," 3ie Publications 2009-3, International Initiative for Impact Evaluation (3ie).
    26. Caliendo, Marco & Mahlstedt, Robert & Mitnik, Oscar A., 2014. "Unobservable, but Unimportant? The Influence of Personality Traits (and Other Usually Unobserved Variables) for the Evaluation of Labor Market Policies," IZA Discussion Papers 8337, Institute of Labor Economics (IZA).
    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. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 871-905, August.
    2. James J. Heckman, 2005. "Micro Data, Heterogeneity and the Evaluation of Public Policy Part 2," The American Economist, Sage Publications, vol. 49(1), pages 16-44, March.
    3. Stephan Thomsen, 2009. "Job Search Assistance Programs in Europe: Evaluation Methods and Recent Empirical Findings," FEMM Working Papers 09018, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    4. 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.
    5. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
    6. Jones A.M & Rice N, 2009. "Econometric Evaluation of Health Policies," Health, Econometrics and Data Group (HEDG) Working Papers 09/09, HEDG, c/o Department of Economics, University of York.
    7. Bernhard Boockmann & Tobias Brändle, 2019. "Coaching, Counseling, Case‐Working: Do They Help the Older Unemployed Out of Benefit Receipt and Back Into the Labor Market?," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 436-468, November.
    8. Caliendo, Marco & Mahlstedt, Robert & Mitnik, Oscar A., 2017. "Unobservable, but unimportant? The relevance of usually unobserved variables for the evaluation of labor market policies," Labour Economics, Elsevier, vol. 46(C), pages 14-25.
    9. Peter R. Mueser & Kenneth R. Troske & Alexey Gorislavsky, 2007. "Using State Administrative Data to Measure Program Performance," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 761-783, November.
    10. Carlos A. Flores & Oscar A. Mitnik, 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," Working Papers 2010-10, University of Miami, Department of Economics.
    11. Aakvik, Arild & Heckman, James J. & Vytlacil, Edward J., 2005. "Estimating treatment effects for discrete outcomes when responses to treatment vary: an application to Norwegian vocational rehabilitation programs," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 15-51.
    12. Hagen, Tobias, 2016. "Econometric Evaluation of a Placement Coaching Program for Recipients of Disability Insurance Benefits in Switzerland," VfS Annual Conference 2016 (Augsburg): Demographic Change 145736, Verein für Socialpolitik / German Economic Association.
    13. Ravallion, Martin, 2008. "Evaluating Anti-Poverty Programs," Handbook of Development Economics, in: T. Paul Schultz & John A. Strauss (ed.), Handbook of Development Economics, edition 1, volume 4, chapter 59, pages 3787-3846, Elsevier.
    14. Justine Burns & Malcolm Kewsell & Rebecca Thornton, 2009. "Evaluating the Impact of Health Programmes," SALDRU Working Papers 40, Southern Africa Labour and Development Research Unit, University of Cape Town.
    15. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    16. Hagen, Tobias, 2016. "Econometric evaluation of a placement coaching program for recipients of disability insurance benefits in Switzerland," Working Paper Series 10, Frankfurt University of Applied Sciences, Faculty of Business and Law.
    17. Yonatan Eyal, 2010. "Examination of the Empirical Research Environment of Program Evaluation: Methodology and Application," Evaluation Review, , vol. 34(6), pages 455-486, December.
    18. James J. Heckman, 1991. "Randomization and Social Policy Evaluation Revisited," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
    19. Heckman, James, 2001. "Accounting for Heterogeneity, Diversity and General Equilibrium in Evaluating Social Programmes," Economic Journal, Royal Economic Society, vol. 111(475), pages 654-699, November.
    20. James J. Heckman & Jeffrey A. Smith, 1999. "The Pre-Program Earnings Dip and the Determinants of Participation in a Social Program: Implications for Simple Program Evaluation Strategies," NBER Working Papers 6983, National Bureau of Economic Research, Inc.

    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:sae:sagope:v:10:y:2020:i:1:p:2158244019898815. 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: SAGE Publications (email available below). General contact details of provider: .

    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.