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This article presents findings from a cost-benefit analysis of the Tulsa Individual Development Account (IDA) program, a demonstration program that was initiated in the late 1990s and is being evaluated through random assignment. The program put particular emphasis on using savings subsidies to help participants accumulate housing assets. The key follow-up data used in the evaluation was collected around 10 years after random assignment, about 6 years after the program ended. The results imply that, during this 10-year observation period, program participants gained from the program and that the program resulted in net costs to the government and private donors, and that society as a whole was probably worse off as a consequence of the program. The article examines in some detail whether these findings are robust to a number of different considerations, including the assumptions upon which the results depend, uncertainly reflected by the standard errors of the impact estimates used to derive the benefits and costs, and omitted benefits and costs, and concludes that they are essentially robust. For example, a Monte Carlo analysis suggests that the probability that the societal net benefits of the Tulsa program were negative during the observation period is over 90% and that the probability that the loss to society exceeded $1000 is 80%. Further analysis considered benefits and costs that might occur beyond the observation period. Based on this analysis, it appeared plausible, although far from certain, that the societal net benefits of the Tulsa program could eventually become positive. This would occur if the program's apparent positive net impact on educational attainment generates substantial positive effects on the earnings of program participants after the observation period ended. However, there was no evidence that the educational impacts had yet begun to produce positive effects on earnings by the end of the observation period.

During the 1980s, a number of states operated welfare‐to‐work programs on a demonstration basis and subjected these demonstrations to formal cost/benefit evaluations. This paper examines the evaluators' methods and summarizes and interprets their findings. Cost/benefit analysis of welfare‐to‐work programs can provide a rough but useful assessment of a program's efficiency in reducing welfare caseloads. But the evaluation results are more difficult to interpret than they may appear to be. For example, the results typically imply that such programs produce small net gains to society when gains and losses are measured in terms of net income. However, a sensitivity analysis measuring net gains and losses to welfare recipients in terms of changes in net utility suggests that an important modification to the evaluators' methodology might well reverse this finding in many instances.

AbstractEstimates of the elasticity of the marginal utility of income are necessary for determining distributional weights to correct for diminishing marginal utility of income, which is particularly important in light of increasing concern about accounting for distributional impacts in regulatory review. The elasticity is also necessary for computing the social discount rate using the Ramsey formula. Despite many attempts to estimate the elasticity of the marginal utility of income, considerable uncertainty exists about the magnitude of this key parameter. In this paper, we use meta-analysis of estimates of the elasticity from the US and UK to shed light on the appropriate elasticity values to use for both distributional weighting and discounting. Relying on our findings, we tentatively conclude that it is reasonable to base the social discount rate and distributional weights on an elasticity of 1.6, with lower- and upper-bound sensitivity testing at 1.2 and 2.0. This estimate results in distributional weights which appear plausible, and which we believe can contribute to a consensus on how to conduct distributional weighting. Moreover, the resulting social discount rate is within the range typically recommended when the Ramsey formula is used.

The Employment Retention and Advancement (ERA) Demonstration programme is a major current welfare-to-work social experiment, the largest random allocation evaluation ever mounted in Great Britain. This article draws on experience gained in designing the ERA Demonstration to explore the strengths and limitations of social experimentation for policy evaluation and analysis. The focus of the discussion is on the reasons for the choice of random allocation as a mean of estimating programme impacts, contrasting this approach with the alternatives. The weaknesses of random allocation designs are also examined in the light of the types of information policy-makers require from evaluations of labour market programmes and social policy demonstrations. The perennial 'black box' problem and the difficulties in generalizing from social experiments are given particular prominence.

AbstractThere are increasing calls for concrete suggestions on how to account for distributional impacts in policy analysis. Within the context of benefit-cost analysis, per se, one possibility is to apply "distributional weights," to inflate costs and benefits experienced by poor or disadvantaged groups. We distinguish between "utility-weights," intended to correct for the bias in willingness to pay caused by diminishing marginal utility of income, and "equity-weights," intended to account for the possibility that decision makers might have disproportional concern about the welfare of the poor or other disadvantaged groups. We argue that utility-weights are appropriate and necessary to maintain the legitimacy of BCA as a measure of aggregate welfare, but that equity-weights are inappropriate because they involve moral judgments that should remain in the domain of democratically accountable decision makers, and because they conflate information about both the welfare and equity impacts of policies, making it impossible for decision-makers to apply their own moral values to the assessment of tradeoffs between welfare and equity. We offer concrete suggestions regarding the application of utility-weights and the calculation of a set of metrics to provide intuitively comprehensible and useful information about, and allow decision makers to quantitatively assess the tradeoffs between, welfare and equity caused by specific policies.