IDEAS home Printed from https://ideas.repec.org/a/pal/easeco/v42y2016i2p252-269.html
   My bibliography  Save this article

Improved Targeting of Social Programs: An Application to a State Job Coaching Program for Adults with Intellectual Disabilities

Author

Listed:
  • Melayne Morgan McInnes

    (Department of Economics, Darla Moore School of Business, University of South Carolina, 1014 Greene Street, Columbia, SC 29208, USA)

  • Orgul Demet Ozturk

    (Department of Economics, Darla Moore School of Business, University of South Carolina, 1014 Greene Street, Columbia, SC 29208, USA)

  • Suzanne McDermott

    (Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Discovery 1 Columbia, SC 29208, USA)

  • Joshua Mann

    (Department of Family and Preventive Medicine, School of Medicine, University of South Carolina, 3209 Colonial Drive, Columbia, SC 29203, USA)

Abstract

In a climate of flat or shrinking budgets, can programs reallocate existing resources to improve efficiency? We illustrate the potential for gains from redirecting resources using data from a state job coaching program that is designed to increase employment among adults with intellectual disabilities (IDs). We model selection into the program and employment outcomes for participants and non-participants allowing for potentially heterogeneous response among observationally equivalent individuals. In our simulations, we find that state ID population employment can be increased from 10.7 percent to an upper bound of 16.7 percent by a program administrator who can allocate the job coaches to those with the most to gain. This is a 56 percent increase in the overall employment rate. While we assume that program administrators know more about individual program participants than we do, we can consider an administrator who has only the information available to the econometrician. In this case, targeting gains based only on observable characteristics would lead to 11.8 percent employment, which is an 11 percent increase in the overall employment rate. Surprisingly, a simple rule that only requires administrators to predict employment success when treated (based on observables) will achieve almost the same results.

Suggested Citation

  • Melayne Morgan McInnes & Orgul Demet Ozturk & Suzanne McDermott & Joshua Mann, 2016. "Improved Targeting of Social Programs: An Application to a State Job Coaching Program for Adults with Intellectual Disabilities," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 42(2), pages 252-269, March.
    • Handle: RePEc:pal:easeco:v:42:y:2016:i:2:p:252-269
    as

    Download full text from publisher

    File URL: http://www.palgrave-journals.com/eej/journal/v42/n2/pdf/eej201445a.pdf
    File Function: Link to full text PDF
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: http://www.palgrave-journals.com/eej/journal/v42/n2/full/eej201445a.html
    File Function: Link to full text HTML
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. James J. Heckman & Carolyn J. Heinrich & Pascal Courty & Gerald Marschke & Jeffrey Smith (ed.), 2011. "The Performance of Performance Standards," Books from Upjohn Press, W.E. Upjohn Institute for Employment Research, number tpps, November.
    2. James J. Heckman & Carolyn Heinrich & Jeffrey Smith, 2002. "The Performance of Performance Standards," Journal of Human Resources, University of Wisconsin Press, vol. 37(4), pages 778-811.
    3. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    4. Heckman, James J. & Vytlacil, Edward J., 2000. "The relationship between treatment parameters within a latent variable framework," Economics Letters, Elsevier, vol. 66(1), pages 33-39, January.
    5. Melayne Morgan McInnes & Orgul Demet Ozturk & Suzanne McDermott & Joshua R. Mann, 2010. "Does supported employment work?," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 29(3), pages 506-525.
    6. 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.
    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. Pedro Carneiro & James J. Heckman & Edward J. Vytlacil, 2011. "Estimating Marginal Returns to Education," American Economic Review, American Economic Association, vol. 101(6), pages 2754-2781, October.
    2. 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.
    3. 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.
    4. Daniel Polsky & Anirban Basu, 2012. "Selection Bias in Observational Data," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 46, Edward Elgar Publishing.
    5. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, February.
    6. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157.
    7. Fitzenberger, Bernd & Furdas, Marina & Sajons, Christoph, 2016. "End-of-year spending and the long-run employment effects of training programs for the unemployed," Freiburg Discussion Papers on Constitutional Economics 16/08, Walter Eucken Institut e.V..
    8. Fredrik Andersson & Harry J. Holzer & Julia I. Lane & David Rosenblum & Jeffrey Smith, 2024. "Does Federally Funded Job Training Work? Nonexperimental Estimates of WIA Training Impacts Using Longitudinal Data on Workers and Firms," Journal of Human Resources, University of Wisconsin Press, vol. 59(4), pages 1244-1283.
    9. Christian Volpe Martincus, 2010. "Odyssey in International Markets: An Assessment of the Effectiveness of Export Promotion in Latin America and the Caribbean," IDB Publications (Books), Inter-American Development Bank, number 16458, February.
    10. Felix Koenig & Barbara Petrongolo & John Van Reenen & Nitika Bagaria, 2019. "Can Helping the Sick Hurt the Able? Incentives, Information and Disruption in a Welfare Reform," The Economic Journal, Royal Economic Society, vol. 129(624), pages 3189-3218.
    11. Brian Krogh Graversen & Peter Jensen, 2010. "A Reappraisal of the Virtues of Private Sector Employment Programmes," Scandinavian Journal of Economics, Wiley Blackwell, vol. 112(3), pages 546-569, September.
    12. 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.
    13. James J. Heckman, 2008. "The Principles Underlying Evaluation Estimators with an Application to Matching," Annals of Economics and Statistics, GENES, issue 91-92, pages 9-73.
    14. Deborah A. Cobb‐Clark & Thomas Crossley, 2003. "Econometrics for Evaluations: An Introduction to Recent Developments," The Economic Record, The Economic Society of Australia, vol. 79(247), pages 491-511, December.
    15. James Heckman & Justin L. Tobias & Edward Vytlacil, 2001. "Four Parameters of Interest in the Evaluation of Social Programs," Southern Economic Journal, John Wiley & Sons, vol. 68(2), pages 210-223, October.
    16. Mingliang Li & Dale J. Poirier & Justin L. Tobias, 2004. "Do dropouts suffer from dropping out? Estimation and prediction of outcome gains in generalized selection models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(2), pages 203-225.
    17. Philipp Eisenhauer & James J. Heckman & Edward Vytlacil, 2015. "The Generalized Roy Model and the Cost-Benefit Analysis of Social Programs," Journal of Political Economy, University of Chicago Press, vol. 123(2), pages 413-443.
    18. Ismaël Mourifié & Marc Henry & Romuald Méango, 2020. "Sharp Bounds and Testability of a Roy Model of STEM Major Choices," Journal of Political Economy, University of Chicago Press, vol. 128(8), pages 3220-3283.
    19. Jaap Abbring & James Heckman, 2008. "Dynamic policy analysis," CeMMAP working papers CWP05/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Christian Volpe Martincus & Jerónimo Carballo, 2010. "Entering new country and product markets: does export promotion help?," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 146(3), pages 437-467, September.

    More about this item

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • H43 - Public Economics - - Publicly Provided Goods - - - Project Evaluation; Social Discount Rate
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

    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:pal:easeco:v:42:y:2016:i:2:p:252-269. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    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.