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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Paper provided by University of Virginia, Department of Economics in its series Virginia Economics Online Papers with number
356.
Find related papers by JEL classification: C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Statistical Decision Theory; Operations Research H43 - Public Economics - - Publicly Provided Goods - - - Project Evaluation; Social Discount Rate H50 - Public Economics - - National Government Expenditures and Related Policies - - - General I38 - Health, Education, and Welfare - - Welfare and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
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