IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

To Train or Not To Train: Optimal Treatment Assignment Rules Using Welfare-to-Work Experiments

Listed author(s):
  • John V. Pepper


Planners often face the especially difficult and important task of assigning programs or treatments to optimize outcomes. Using the recent welfare-to-work reforms as an illustration, this paper considers the normative problem of how administrators might use data from randomized experiments to assign treatments. Under the new welfare system, state governments must design welfare programs to optimize employment. With experimental results in-hand, planners observe the average effect of training on employment but may not observe the individual effect of training. If the effect of a treatment varies across individuals, the planner faces a decision problem under ambiguity (Manski, 1998). In this setting, I find a straightforward proposition formalizes conditions under which a planner should reject particular decision rules as being inferior. An optimal decision rule, however, may not be revealed. In the absence of strong assumptions about the degree of heterogeneity in the population or the information known by the planner, the data are inconclusive about the efficacy of most assignment rules.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: no

Paper provided by University of Virginia, Department of Economics in its series Virginia Economics Online Papers with number 356.

in new window

Date of creation: Feb 2002
Handle: RePEc:vir:virpap:356
Contact details of provider: Web page:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

in new window

  1. Charles F. Manski & John Newman & John V. Pepper, 2002. "Using Performance Standards to Evaluate Social Programs with Incomplete Outcome Data," Evaluation Review, , vol. 26(4), pages 355-381, August.
  2. Mark C. Berger & Dan Black & Jeffrey Smith, 2000. "Evaluating Profiling as a Means of Allocating Government Services," UWO Department of Economics Working Papers 200018, University of Western Ontario, Department of Economics.
  3. Arulampalam, W. & Robin A. Naylor & Jeremy P. Smith, 2002. "University of Warwick," Royal Economic Society Annual Conference 2002 9, Royal Economic Society.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:vir:virpap:356. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Debby Stanford)

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.