To Train or Not To Train: Optimal Treatment Assignment Rules Using Welfare-to-Work Experiments
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.
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- 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.
- Arulampalam, W. & Robin A. Naylor & Jeremy P. Smith, 2002. "University of Warwick," Royal Economic Society Annual Conference 2002 9, Royal Economic Society.
- 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.
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