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Building in an Evaluation Component for Active Labor Market Programs: A Practitioner's Guide

  • Card, David

    ()

    (University of California, Berkeley)

  • Ibarrarán, Pablo

    ()

    (Inter-American Development Bank)

  • Villa, Juan Miguel

    ()

    (University of Manchester)

The guide outlines the main evaluation challenges associated with ALMP’s, and shows how to obtain rigorous impact estimates using two leading evaluation approaches. The most credible and straightforward evaluation method is a randomized design, in which a group of potential participants is randomly divided into a treatment and a control group. Random assignment ensures that the two groups would have had similar experiences in the post-program period in the absence of the program intervention. The observed post-program difference therefore yields a reliable estimate of the program impact. The second approach is a difference in differences design that compares the change in outcomes between the participant group and a selected comparison group from before to after the completion of the program. In general the outcomes of the comparison group may differ from the outcomes of the participant group, even in the absence of the program intervention. If the difference observed prior to the program would have persisted in the absence of the program, however, then the change in the outcome gap between the two groups yields a reliable estimate of the program impact. This guideline reviews the various steps in the design and implementation of ALMP’s, and in subsequent analysis of the program data, that will ensure a rigorous and informative impact evaluation using either of these two techniques.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 6085.

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Length: 54 pages
Date of creation: Oct 2011
Date of revision:
Handle: RePEc:iza:izadps:dp6085
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  1. David Card & Jochen Kluve & Andrea Weber, 2010. "Active Labor Market Policy Evaluations: A Meta-Analysis," NBER Working Papers 16173, National Bureau of Economic Research, Inc.
  2. Orley Ashenfelter & David Card, 1984. "Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs," NBER Working Papers 1489, National Bureau of Economic Research, Inc.
  3. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
  4. John DiNardo & David S. Lee, 2010. "Program Evaluation and Research Designs," NBER Working Papers 16016, National Bureau of Economic Research, Inc.
  5. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
  6. John DiNardo & David S. Lee, 2010. "Program Evaluation and Research Designs," Working Papers 1228, Princeton University, Department of Economics, Industrial Relations Section..
  7. Duflo, Esther & Glennerster, Rachel & Kremer, Michael, 2007. "Using Randomization in Development Economics Research: A Toolkit," CEPR Discussion Papers 6059, C.E.P.R. Discussion Papers.
  8. V. Joseph Hotz & Guido W. Imbens & Jacob A. Klerman, 2006. "Evaluating the Differential Effects of Alternative Welfare-to-Work Training Components: A Re-Analysis of the California GAIN Program," NBER Working Papers 11939, National Bureau of Economic Research, Inc.
  9. Ashenfelter, Orley C, 1978. "Estimating the Effect of Training Programs on Earnings," The Review of Economics and Statistics, MIT Press, vol. 60(1), pages 47-57, February.
  10. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
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